Object learning and identification using neural networks

ABSTRACT

An object identification method is disclosed. The method includes training a first neural network for a first set of conditions regarding a first plurality of objects, training a second neural network for a second set of conditions regarding a second plurality of objects, receiving a plurality of target images associated with a target set of conditions in which to identify objects, analyzing the plurality of target images using the first and second neural networks to identify objects in the plurality of target images resulting in object identification information, and selecting the first neural network or the second neural network as a preferred neural network for the target set of conditions based on an analysis of the object identification information.

TECHNICAL FIELD

The present disclosure relates generally to the identification,tracking, mapping, and collection of objects from the ground or a field.

BACKGROUND Description of the Related Art

Rocks in agricultural fields present a problem to farmers across thecountry and throughout the world. Rocks can foul up and interfere withthe operation of automated, expensive agricultural equipment, such asmechanized seeders and combines. Rocks also present safety hazards tofarmers and their land, such as from sparks arising from contact withrotating metallic equipment. These issues can result in expensiverepair, lost productivity, and the need for careful planning.

While a number of implements—such as rakes, windrowers, and sieves, orcombinations thereof—can be used to clear fields of rocks and otherobjects, they generally include manual operation and still have a highrate of failure (i.e., they often miss rocks). This failure rate oftenresults in multiple passes by these implements and they are oftensupplemented by human intervention to pick rocks that are left behind.Such manual operation and human picking intervention involvesexpenditure on the labor required, and is often slow and unpleasantwork. It is with respect to these and other considerations that theembodiments described herein have been made.

BRIEF SUMMARY

Embodiments are generally directed to the training and use of neuralnetworks to identify objects in images collected by an image-collectionvehicle. The identified objects are utilized to guide anobject-collection system over the target geographical area towards theobjects to pick up and remove the identified objects from the targetgeographical area.

A method may be summarized as including training a first neural networkfor a first set of conditions regarding a first plurality of objects;training a second neural network for a second set of conditionsregarding a second plurality of objects, wherein the second set ofconditions includes at least one different condition than the first setof conditions; receiving a plurality of target images associated with athird set of conditions in which to identify objects; analyzing theplurality of target images using the first and second neural networks toidentify objects in the plurality of target images resulting in objectidentification information; and selecting the first neural network orthe second neural network as a preferred neural network for the thirdset of conditions based on an analysis of the object identificationinformation. Analyzing the plurality of target images using the firstand second neural networks may include analyzing the plurality of targetimages using the first neural network to identify objects in theplurality of target images resulting in a first set of objectidentification information; analyzing the plurality of target imagesusing the second neural network to identify objects in the plurality oftarget images resulting in a second set of object identificationinformation; and identifying differences between the first and secondsets of object identification information based on a comparison betweenthe first set of object identification information and the second set ofobject identification information. Analyzing the plurality of targetimages using the first and second neural networks may include analyzinga first set of target images from the plurality of target images usingthe first neural network to identify objects in the first set targetimages resulting in a first set of object identification information;analyzing a second set of target images from the plurality of targetimages using the second neural network to identify objects in the secondset of target images resulting in a second set of object identificationinformation; and comparing the first set of object identificationinformation with the second set of object identification information toidentify differences between the first and second sets of objectidentification information.

The method may further include alternatingly selecting the first andsecond sets of target images from the plurality of target images basedon a select number of images for each selection. The select number ofimages may be between one and five target images. Selecting the firstneural network or the second neural network as the preferred neuralnetwork may include selecting the preferred neural network based on anaccuracy of the first neural network and an accuracy of the secondneural network. Selecting the first neural network or the second neuralnetwork as the preferred neural network may include selecting the firstneural network as the preferred neural network in response to a numberof positive identifications using the first neural network being higherthan a number of positive identifications for the second neural network,or the number of false-positive identifications using the first neuralnetwork being lower than the number of false-positives using the secondneural network; and selecting the second neural network as the preferredneural network in response to the number of positive identificationsusing the second neural network being higher than the number of positiveidentifications for the first neural network, or the number offalse-positive identifications using the second neural network beinglower than the number of false-positives using the first neural network.Selecting the first neural network or the second neural network as thepreferred neural network may include providing the first and second setsof object identification information to a reviewer; and receiving aselection of the preferred neural network from the reviewer based on thefirst and second sets of object identification information.

The method may further include receiving a second plurality of targetimages associated with a fourth set of conditions; and predicting use ofthe first neural network or the second neural network to analyze thesecond plurality of target images based on a comparison between thefourth set of conditions and first, second, and third sets ofconditions. Predicting use of the first neural network or the secondneural network further may include determining separate closenessfactors between the fourth set of conditions and each of the first,second, and third set of conditions; selecting a highest closenessfactor from the determined closeness factors; in response to the highestcloseness factor being associated with the first set of conditions,analyzing the second plurality of target images using the first neuralnetwork to identify objects in the second plurality of target images; inresponse to the highest closeness factor being associated with thesecond set of conditions, analyzing the second plurality of targetimages using the second neural network to identify objects in the secondplurality of target images; and in response to the highest closenessfactor being associated with the third set of conditions, analyzing thesecond plurality of target images using the preferred neural network toidentify objects in the second plurality of target images.

The first and second sets of conditions may include at least two of: ageographical area, expected dirt type found in the geographical area,expected object type found in the geographical area, type of crop beingplanted at the geographical area, status of the crop, time of year,weather at time of image capture, and expected type or amount ofnon-cultivated vegetation. Training the first neural network may includereceiving a first selection of the first set of conditions from a user;obtaining a first set of training images associated with the first setof conditions; and training a first neural network using the first setof training images.

A computing device may be summarized as including a neural networkdatabase for storing a trained neural network data for a first andsecond neural network; a processor; and a memory that stores computerinstructions that, when executed by the processor, cause the processorto: obtain a first set of training images associated with the first setof conditions regarding a first plurality of objects; train a firstneural network using the first set of training images; obtain a secondset of training images associated with the second set of conditionsregarding a second plurality of objects, wherein the second set ofconditions includes at least one different condition than the first setof conditions; train a second neural network using the second set oftraining images; receive a plurality of target images associated with athird set of conditions regarding a third ground area state in which toidentify objects; analyze the plurality of target images using the firstneural network to identify objects in the plurality of target imagesresulting in a first set of identification information; analyze theplurality of target images using the second neural network to identifyobjects in the plurality of target images resulting in a second set ofidentification information; and select the first neural network or thesecond neural network as a preferred neural network for the third set ofconditions based on a comparison between the first and second sets ofidentification information.

Execution of the computer instructions by the processor to analyze theplurality of target images using the first and second neural network maycause the processor to: alternatingly select a first set of targetimages and a second set of target images from the plurality of targetimages; analyze the first set of target images using the first neuralnetwork to identify objects in the first set of target images; andanalyze the second set of target images using the second neural networkto identify objects in the second set of target images. Execution of thecomputer instructions by the processor to select the first neuralnetwork or the second neural network as the preferred neural network maycause the processor to: select the preferred neural network based on anaccuracy of the first neural network and an accuracy of the secondneural network. Execution of the computer instructions by the processormay cause the processor to: receive a fourth set of conditions in whichto identify objects; and predict use of the first neural network or thesecond neural network based on a comparison between the fourth set ofconditions and first, second, and third sets of conditions. The firstand second sets of conditions may include at least one of: geologicalinformation, topographical information, biological information, orclimatic information.

A system may be summarized as including a first non-transitorycomputer-readable storage medium that stores first computer instructionsthat, when executed by a first processor on a first computer device,cause the first processor to: receive a plurality of images associatedwith a corresponding plurality of object picking conditions; and train aplurality of neural networks for the plurality of object pickingconditions based on the corresponding plurality of images; and a secondnon-transitory computer-readable storage medium that stores secondcomputer instructions that, when executed by a second processor on asecond computer device, cause the second processor to: receive aplurality of target images associated with a target set of objectpicking conditions; analyze the plurality of target images using theplurality of neural networks to identify objects in the plurality oftarget images; and select, from the plurality of neural network, apreferred neural network for the target set of conditions based on theneural network analysis. The first computer device and the secondcomputer device may be a same computer device. The correspondingplurality of conditions may include at least one of: a geographicallocation, expected dirt type, expected object type, type of crop, statusof the crop, time of year, weather conditions, expected type of non-cropvegetation, or expected amount of non-crop vegetation.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Non-limiting and non-exhaustive embodiments are described with referenceto the following drawings. In the drawings, like reference numeralsrefer to like parts throughout the various figures unless otherwisespecified.

For a better understanding of the present invention, reference will bemade to the following Detailed Description, which is to be read inassociation with the accompanying drawings:

FIGS. 1A-1B are example illustrations of a drone analyzing a field toidentify and map objects such that the mapped objects are viewable on amobile computing device in accordance with embodiments described herein;

FIG. 1C is an example image portion that a includes representation of anobject that was captured by a drone in accordance with embodimentsdescribed herein;

FIG. 1D is a top view example illustration of a position of a dronerelative to a location of a captured image of a field in accordance withembodiments described herein;

FIGS. 2A-2C are example illustrations of overlaying images to identifythe same object in multiple images in accordance with embodimentsdescribed herein;

FIGS. 3A-3B are example illustrations of various embodiments of anobject object-collection system in accordance with embodiments describedherein;

FIGS. 4A-4B are example illustrations of images captured by an objectobject-collection system to identify, track, and pick up objects inaccordance with embodiments described herein;

FIG. 4C is an example illustration of an image utilizing specific imageareas to identify the speed of the object-collection system inaccordance with embodiments described herein;

FIG. 4D is an example illustration of a graph showing the tracked speedof the object-collection system in accordance with embodiments describedherein;

FIG. 5 is an example illustration of a display presented to a driver ofthe object-collection system in accordance with embodiments describedherein;

FIG. 6A illustrates a context diagram of a system for scanning a targetgeographical area, identifying objects in that area, and employing anobject-collection system to pick up the objects in accordance withembodiments described herein;

FIG. 6B shows a system diagram that describes one implementation ofcomputing systems for implementing embodiments described herein;

FIG. 7 illustrates a logical flow diagram showing one embodiment of aprocess for instructing an image-collection vehicle to scan a targetgeographical area in accordance with embodiments described herein;

FIG. 8 illustrates a logical flow diagram showing one embodiment of aprocess for identifying and mapping objects in a target geographicalarea in accordance with embodiments described herein;

FIG. 9 illustrates a logical flow diagram showing one embodiment of aprocess for identifying objects and guiding an object-collection systemto pick up the identified objects in a target geographical area inaccordance with embodiments described herein;

FIGS. 10A-10B illustrate a logical flow diagram showing one embodimentof a process for modifying captured images to enable identification ofobjects in accordance with embodiments described herein;

FIG. 11 illustrates a logical flow diagram showing one embodiment of aprocess for removing duplicative identifications of objects inaccordance with embodiments described herein;

FIG. 12 illustrates a logical flow diagram showing one embodiment of aprocess for employing multiple artificial neural network to select apreferred neural network in accordance with embodiments describedherein;

FIG. 13 illustrates a logical flow diagram showing one embodiment of aprocess for predicting and selecting an artificial neural network to usebased on specific conditions of the target geographical area and theexpected objects in accordance with embodiments described herein;

FIG. 14 illustrates a logical flow diagram showing one embodiment of aprocess for selecting an artificial neural network to employ on zones ofa target geographical area to identify objects in accordance withembodiments described herein;

FIG. 15 illustrates a logical flow diagram showing one embodiment of aprocess for guiding an object object-collection system to pick uppreviously identified objects in a target geographical area inaccordance with embodiments described herein;

FIG. 16A illustrates a perspective view of an object picking assemblythat includes a pair of travelers that move along a rail, with eachtraveler respectively associated with a head assembly;

FIG. 16B illustrates a top view of an object picking assembly thatincludes a pair of travelers that move along a rail, with each travelerrespectively associated with a head assembly;

FIG. 16C illustrates a side view of an object picking assembly thatincludes a pair of travelers that move along a rail, with each travelerrespectively associated with a head assembly;

FIG. 17A illustrates a perspective view of rollers that are generallycylindrical with a taper from the edges of the rollers toward a centralportion of the rollers;

FIG. 17B illustrates another embodiment of the object picking assemblypositioned over an object such that the object is between the headassemblies which have the respective arms and rollers;

FIG. 17C illustrates the embodiment of FIG. 17B, wherein the headassemblies are rotated inward toward the object, such that the rollersengage the object and lift the object from the field and between thearms;

FIG. 18A illustrates the embodiment of FIG. 17B, wherein the rollerscontinue engaging the object and lifting the object between the arms;

FIG. 18B illustrates the embodiment of FIG. 17B, wherein the rollerscontinue engaging the object and lifting the object between the arms andinto the bucket;

FIG. 18C illustrates the embodiment of FIG. 17B, wherein the rollersfinish depositing the object into the bucket and return to their initialpositions;

FIG. 19A illustrates a perspective view of another embodiment of theobject picking assembly that includes a pair of travelers that movealong a rail, with each traveler respectively coupled with a paddleassembly via an arm;

FIG. 19B illustrates an end view of another embodiment of the objectpicking assembly that includes a pair of travelers that move along arail, with each traveler respectively coupled with a paddle assembly viaan arm, wherein the travelers move the paddle assemblies to narrow thecavity;

FIG. 19C illustrates an end view of another embodiment of the objectpicking assembly that includes a pair of travelers that move along arail, with each traveler respectively coupled with a paddle assembly viaan arm, wherein the travelers move the paddle assemblies to widen thecavity;

FIG. 20A illustrates a side view of another embodiment of the objectpicking assembly that includes paddle assemblies coupled to a bucket viaa rail coupled to a front end of the bucket, and two or more paddleassemblies coupled to a bucket in a more upright orientation;

FIG. 20B illustrates a side view of another embodiment of the objectpicking assembly that includes paddle assemblies coupled to a bucket viaa rail coupled to a front end of the bucket, and two or more paddleassemblies coupled to a bucket in a more reclined orientation;

FIG. 21A illustrates a perspective view of another embodiment of theobject picking assembly that includes paddle assemblies coupled to arail, with the rail being offset from and coupled to the bucket via barscoupled to sidewalls of the bucket;

FIG. 21B illustrates a side cutaway view of another embodiment of theobject picking assembly that includes paddle assemblies coupled to arail, with the rail being offset from and coupled to the bucket via barscoupled to sidewalls of the bucket;

FIG. 22A illustrates a perspective view of another embodiment of theobject picking assembly that includes a first and second paddle assemblycoupled to a single traveler, which can move along a rail that issuspended above and forward of the front edge of a bucket;

FIG. 22B illustrates an end view of another embodiment of the objectpicking assembly that includes a first and second paddle assemblycoupled to a single traveler, which can move along a rail that issuspended above and forward of the front edge of a bucket;

FIG. 22C illustrates an end view of another embodiment of the objectpicking assembly that includes a first and second paddle assemblycoupled to a single traveler, which can move along a rail that issuspended above and forward of the front edge of a bucket with thepaddle assemblies tilted;

FIG. 23A illustrates a side view of another embodiment of the objectpicking assembly that includes a first and second paddle assemblycoupled to a single traveler, which can move along a rail that issuspended above and forward of the front edge of a bucket;

FIG. 23B illustrates a side view of another embodiment of the objectpicking assembly that includes a first and second paddle assemblycoupled to a single traveler, which can move along a rail that issuspended above and forward of the front edge of a bucket with thepaddle assemblies tilted;

FIG. 24A illustrates another embodiment of an object picking assemblythat includes a rail coupled to a front edge of a bucket via a pair ofclamps, such that the rail is offset from the front edge of the bucket,and a tine assembly is coupled to the rail via a rail cuff;

FIG. 24B illustrates another embodiment of an object picking assemblythat includes a rail coupled to a front edge of a bucket via a pair ofclamps, such that the rail is offset from the front edge of the bucketand a tine assembly is coupled to the rail via a rail cuff, the tineassembly including a plurality of tines;

FIG. 25A illustrates a perspective view of another embodiment of anobject picking assembly that includes a rail coupled to a front edge ofa bucket via a pair of clamps, wherein a given rail cuff is associatedwith a plurality of tines that define a tine unit, including cross barsthat couple one or more of the tines of the tine unit;

FIG. 25B illustrates a perspective view of another embodiment of arotating object picking assembly that includes a rail coupled to a frontedge of a bucket via a pair of clamps, wherein a given rail cuff isassociated with a plurality of tines that define a tine unit, includingcross bars that couple one or more of the tines of the tine unit;

FIG. 26A illustrates a perspective view of another embodiment of railcuff associated with a plurality of tines that define a tine unit,including cross bars that couple one or more of the tines of the tineunit;

FIG. 26B illustrates a side view of another embodiment of an objectpicking assembly that includes a rail coupled to a front edge of abucket via a pair of clamps, wherein the tine unit is coupled at thefront edge of a bucket via a clamp, and the rail cuff can be configuredto rotate the tine unit, which can be desirable for scooping objectsfrom a field and depositing them in the cavity of the bucket;

FIG. 27A illustrates a side view of another embodiment of an objectpicking assembly that includes a tine assembly having a sleeve disposedabout a rack and pinion with one or more tines disposed at a front endof the rack, wherein the tines are retracted;

FIG. 27B illustrates a side view of another embodiment of an objectpicking assembly that includes a tine assembly having a sleeve disposedabout a rack and pinion with one or more tines disposed at a front endof the rack, wherein the tines are extended to scope and pick up anobject;

FIG. 27C illustrates a side view of another embodiment of an objectpicking assembly that includes a tine assembly having a sleeve disposedabout a rack and pinion with one or more tines disposed at a front endof the rack, wherein the tines are retracted after scoping and pickingup an object;

FIG. 28A illustrates a side view of another embodiment of an objectpicking assembly that includes an object picking assembly in a readyposition having a tine assembly configured to react to contacting animmovable object, which in a large object disposed within a field withonly a small portion of the object visible from the surface;

FIG. 28B illustrates a side view of another embodiment of an objectpicking assembly that includes an object picking assembly in an extendedposition having a tine assembly configured to react to contacting animmovable object, which in a large object disposed within a field withonly a small portion of the object visible from the surface;

FIG. 29A illustrates a perspective view of another embodiment of anobject picking assembly that includes a tine assembly having a pluralityof tines which spirally encircle and are coupled to a rail;

FIG. 29B illustrates a side view of another embodiment of an objectpicking assembly that includes a tine assembly having a plurality oftines which spirally encircle and are coupled to a rail with rotationenabling deposition of the object;

FIG. 29C illustrates a side view of another embodiment of an objectpicking assembly that includes a tine assembly having a plurality oftines which spirally encircle and are coupled to a rail with furtherrotation enabling deposition of the object;

FIG. 30A illustrates a perspective view of another embodiment of anobject picking assembly that includes a rim that defines slots viaalternating flanges along the length of the rim;

FIG. 30B illustrates a side view of another embodiment of an objectpicking assembly that includes a rim that defines slots via alternatingflanges along the length of the rim, with one or more tines configuredto engage an object in a field;

FIG. 31A illustrates a side view of another embodiment of an objectpicking assembly that includes a tine assembly having a plurality oftines that are actuated via one or more cylinders;

FIG. 31B illustrates a side view of still another embodiment of anobject picking assembly that includes a tine assembly having a pluralityof tines that are actuated via one or more cylinders;

FIG. 31C illustrates a side view of yet another embodiment of an objectpicking assembly that includes a tine assembly having a plurality oftines that are actuated via one or more cylinders;

FIG. 32A illustrates a side view of yet another embodiment of an objectpicking assembly coupled to or extending from a front end of a bucketincluding an arm and a tine coupled to and configured to translate alongthe length of the arm between a distal end of the arm and a base end ofthe arm;

FIG. 32B illustrates a side view of yet another embodiment of an objectpicking assembly that includes a tine assembly having a first linkagethat extends from a first joint coupled within the cavity of a bucket toa second joint;

FIG. 33 illustrates a side view of yet another embodiment of an objectpicking assembly that includes a pusher assembly having a bar with afirst and second ends;

FIG. 34A illustrates a top view of yet another embodiment of an objectpicking assembly disposed at a front end of a vehicle with a second andthird object picking assembly disposed on sides of the of the vehicle.

FIG. 34B illustrates a perspective view of yet another embodiment of anobject picking assembly disposed at a front end of a vehicle andconfigured to pick up objects and deposit the objects onto a conveyorbelt, which can convey the objects into a cavity of a container in thevehicle.

FIG. 35 illustrates a perspective view of an object-collection systemhaving a vehicle, a bucket, and a two paddle object picking assemblywith a multilink telescoping picking arm;

FIG. 36 illustrates a perspective view of an object-collection systemhaving a bucket, and a two paddle object picking assembly with atelescoping picking arm;

FIG. 37 illustrates a perspective view of an object-collection systemhaving a bucket, and a three paddle object picking assembly with atelescoping picking arm and a hinge on the third paddle;

FIG. 38 illustrates a perspective view of an object-collection systemhaving a bucket, and a two paddle object picking assembly with twotelescoping picking arms;

FIG. 39 illustrates a perspective view of an object-collection systemhaving a bucket, and a three paddle object picking assembly with alateral sliding mechanism and a hinge on the third paddle;

FIG. 40 illustrates a perspective view of an object-collection systemhaving a vehicle, a bucket, and a two paddle object picking assemblywith a lateral sliding mechanism;

FIGS. 41A-41C illustrate various views of an object-collection systemhaving a three paddle object picking assembly with a moving belts on thethird paddle;

FIGS. 42-43 illustrate additional views of an object-collection systemhaving a three paddle object picking assembly with a moving belts on thethird paddle;

FIGS. 44, 45A, 45B, and 45C illustrate various views of anobject-collection system having a two paddle object picking assemblywith a two moving belts on each of the two paddles.

DETAILED DESCRIPTION

The following description, along with the accompanying drawings, setsforth certain specific details in order to provide a thoroughunderstanding of various disclosed embodiments. However, one skilled inthe relevant art will recognize that the disclosed embodiments may bepracticed in various combinations, without one or more of these specificdetails, or with other methods, components, devices, materials, etc. Inother instances, well-known structures or components that are associatedwith the environment of the present disclosure, including but notlimited to the communication systems and networks, have not been shownor described in order to avoid unnecessarily obscuring descriptions ofthe embodiments. Additionally, the various embodiments may be methods,systems, media, or devices. Accordingly, the various embodiments may beentirely hardware embodiments, entirely software embodiments, orembodiments combining software and hardware aspects.

Throughout the specification, claims, and drawings, the following termstake the meaning explicitly associated herein, unless the contextclearly dictates otherwise. The term “herein” refers to thespecification, claims, and drawings associated with the currentapplication. The phrases “in one embodiment,” “in another embodiment,”“in various embodiments,” “in some embodiments,” “in other embodiments,”and other variations thereof refer to one or more features, structures,functions, limitations, or characteristics of the present disclosure,and are not limited to the same or different embodiments unless thecontext clearly dictates otherwise. As used herein, the term “or” is aninclusive “or” operator, and is equivalent to the phrases “A or B, orboth” or “A or B or C, or any combination thereof,” and lists withadditional elements are similarly treated. The term “based on” is notexclusive and allows for being based on additional features, functions,aspects, or limitations not described, unless the context clearlydictates otherwise. In addition, throughout the specification, themeaning of “a,” “an,” and “the” include singular and plural references.

FIGS. 1A-1B are example illustrations of a drone analyzing a field toidentify and map objects such that the mapped objects are viewable on amobile computing device in accordance with embodiments described herein.Beginning with FIG. 1A is an example illustration of an environment 100where a drone 105 is scanning a field 110 using one or more sensors (notillustrated) to capture images of objects located in the field 110. Thefield 110 is a target geographical area that is to be scanned by thedrone 105. The target geographical area may be a field, a plot or tractof land, an orchard, plains, a residential or commercial lot,grasslands, a pasture, a range, a garden, farmland, or other type ofsurveyable land area. For convenience in describing some embodimentsherein, the target geographical area may be generally referred to as afield, such as field 110.

The objects described herein may be natural objects, such as rocks,boulders, weeds, or logs; manmade objects, such as hay bales, golfballs, or baseballs; fruits or vegetables, such as watermelons,cantaloupe, honeydew melon, squash, pumpkins, zucchini, or cucumbers; orother pickable or collectable objects (e.g., animal excrement, garbage,debris, etc.). Objects may be small in size, such as golf ball tobasketball size, or they may be large in size, such as hay bales orlogs. In various embodiments, small objects are those objects with asize or weight below a selected threshold, and large objects are thoseobjects with a size or weight above the selected threshold. The selectedthreshold may be set by a user or administrator and may be pre-selectedor adjusted real time as a target geographical area is being scanned oras objects are being collected.

As described herein, objects are identified in a target geographicalarea such that those objects can be picked up or collected. Use of theterms pick up and collect may be used interchangeably and may includeother forms of gathering or removing objects from the targetgeographical area, including, but not limited to, amassing, compiling,clearing, extracting, or the like.

Briefly, a user selects or otherwise identifies the field 110 to bescanned by the drone 105. The drone 105 flies over the field andcollects sensor data and vehicle positioning data. The data is analyzedto identify and determine a location of objects in the field 110. Agraphical user interface 115 can then be displayed to a user 130 on amobile user device 125 to present a map or images that include visualrepresentations of the objects 120 identified in the field 110. Asdiscussed in more detail herein, the user 130 can manipulate thegraphical user interface 115 to show different types or sizes of objects120, concentrations of objects 120 (e.g., a heat-map view of objectdensity), an optimal path to collect the objects from the field 110, orother information. In some embodiments, the graphical user interface 115can also enable the user 130 to set or modify the field 110 to bescanned by the drone 105.

A representation of the identified objects 120 can be presented to theuser 130 via the graphical user interface 115 on the mobile user device125. Moreover, where a scan of the field 110 includes data regarding thelocation, size, and shape of objects 120 in the field 110, such data canbe overlaid and represented on an image or representation of the field110 (e.g., via graphical user interface 115). The graphical userinterface 115 can include topographical data of the field 110; locationsof the identified objects 120 in the field 110; size of the identifiedobjects 120; shape of the identified objects 120; estimated mass of theidentified objects 120; location of ground features of the field 110(e.g., a pond, stream, field row, field furrow, irrigation channel, andthe like); location of field elements (e.g., a fence line, stump, crops,vegetation, tree, building structure, vehicle, road, sidewalk, pole, andthe like); field characteristics (e.g., moisture content, soil type, andthe like); and any other suitable data regarding the field 110 orelements related to the field 110. In some embodiments, the graphicaluser interface 115 may present the user 130 with graphical controls orother input elements to allow the user 130 to input parameters regardingthe objects. For example, in one embodiment, the graphical userinterface 115 may present a scroll bar or up/down arrows where the user130 can adjust a size parameter of the objects that are represented onthe graphical user interface 115. For example, the user 130 can indicatethat they only want to see objects that are larger than 20 cm(approximately eight inches). As the user 130 manipulates such sizeparameter input, the graphical user interface 115 adds or removes therepresentations of objects 120 as they meet or fall below theuser-selected threshold.

The graphical user interface 115 may also display a heat-mapillustrating clustering or density of objects in the target geographicalarea. In some embodiments, the heat-map may be modified or changed basedon the user's selection of different size parameters of objects in whichto display. In yet other embodiments, the graphical user interface 115may include a representation of a pick-up path or route. In someembodiments, the user 130 can draw or manipulate the graphical userinterface 115 to define the pick-up path over the target geographicalarea. In other embodiments, the pick-up path may be generated based on abest, optimal, or most efficiently calculated path to pick up theobjects 120, such as by utilizing one or more “traveling salesman”algorithms, clustering algorithms, or other path planning or factfinding algorithms. The pick-up path can be utilized by a user or anautonomous controller to instruct or guide an object-collection systemacross the field 110 to pick up and collect at least some of the objects120. For example, FIGS. 3A and 3B illustrate example embodiments of anobject-collection system 300, which is described in more detail below.Although the pick-up path is described as being a best or optimal pathto pick up the identified objects, the pick-up path may also besuboptimal or close to optimal. Moreover, the pick-up path may bedetermined based on one or more user selected options. For example, auser can select a the pick-up path to be a shortest distance, fewestturns greater than 90 degrees, avoid oversized objects, etc.

Scanning of the field 110 can be performed automatically or manually bya user. For example, in some embodiments, the user 130 can manuallyoperate the drone 105 (e.g., via the user device 125 or other remotecontrol) to fly over and scan the field 110 or a portion thereof. Inother embodiments, the user 130 can define a mapping or scanninglocation (e.g., by defining a two- or three-dimensional area via amapping utility of the user device 125), and the user 130 can initiateautomated scanning of the defined mapping location via the drone 105.

In various such embodiments, a user, such as user 130, may input anaddress or GPS coordinates to identify the field 110. A public landdatabase or other records database may be accessed to determine thelegal boundary of the field 110. In other embodiments, the user 130 mayutilize a graphical user interface 115 on the mobile user device 125 toview an image of the field such that the user 130 is enabled to draw inthe boundary of the field 110. Similarly, the user 130 may be enabled todraw, label, or otherwise select exclusion zones in the field 110 thatare not to be scanned by the drone 105. In yet other embodiments, imagerecognition techniques may be employed to identify the boundaries orexclusion zones, or both. As one example of such processing, the imagerecognition techniques may be employed to detect hard edges (e.g., anedge of a field, fence, ditch, etc.) based on color or texture changes,detect houses based on a shape and color of a roof, etc.

In some embodiments, the above described techniques for identifying thefield 110 may be used in combination. For example, the user may inputGPS coordinates, which are used to obtain a satellite image of the field110. The user can then draw in the boundaries or exclusion zones on theimage to define the field 110 (target geographical area) to be scanned.In various embodiments, the boundaries and exclusion zones may bereferred to as or include boundary information, and it may include GPScoordinates labelling scannable areas, GPS coordinates labellingexcluded or non-scannable areas, or other types of information to definea scanning area.

The drone 105 may be any suitable manned or unmanned image-collectionvehicle, such as image-collection vehicle 616 in FIG. 6A. In variousembodiments, the drone 105 uses one or more suitable sensors to scan thefield 110, such as sensor array 622 in FIG. 6A.

Data from the one or more sensors, and data from the drone 105, areanalyzed to identify objects within the field 110, which is described inmore detail herein. Briefly, however, the sensor data may bepre-processed to determine an actual ground location of the sensor dataand to create uniform sensor data. The uniform sensor data can then beinput through one or more artificial neural networks designed toidentify known objects in particular conditions. Once identified, thelocations of the objects are determined based on their position withinthe uniform sensor data and the actual ground location of the sensordata. The locations of the objects are stored in a database (e.g.,object-information database 606 in FIG. 6), along with otherinformation, such as object size, class of the object (e.g., rock,human, animal, etc.), or other information.

Although embodiments described herein are referred to as using one ormore artificial neural networks to identify objects, embodiments are notso limited and other computer vision algorithms or technique may beused. For example, in some embodiments, shape-based algorithms,color-based algorithms, or other visual machine learning techniques maybe employed to identify objects. In some embodiments, the computervision algorithms or techniques may be selected by a user based on thetype of object being identified or the conditions of the targetgeographical area. In yet other embodiments, machine learning techniquesmay be employed to learn which computer vision algorithms or techniquesare most accurate or efficient for a type of object of condition.

In some embodiments, the drone 105 may capture data from a plurality ofsensors, such that their data is utilized in conjunction with each otherto identify the objects. For example, in one embodiment, the drone 105may scan the field 105 using a thermal camera. The thermal data can beanalyzed to identify areas of possible locations of objects. The drone105 can then scan the areas of possible objects using a visual spectrumcamera to rule out or pinpoint the location the objects. Thismulti-spectral data analysis provides many benefits, includingdistinguishing some objects (e.g., rocks) from vegetation and increasingoverall processing speed (e.g., by performing faster, less-complexanalysis on the thermal data and performing slower, more-complexanalysis on only a portion of the field that has a high likelihood ofincluding objects). Although this example describes the use of twosensors during two different scans, embodiments are not so limited.Rather, in other embodiments, more sensors may be utilized, and in yetother embodiments, the sensors may capture data during the same orsubsequent scans of the field 110.

Moreover, some scans for some sensors may be performed at a first heightabove the field 110, while other scans for other sensors may be performat a second height above the field 110, where the second height is lessthan the first height. Furthermore, while the example of FIG. 1Aillustrates a single drone 105, in further embodiments, a plurality ofdrones can be used to scan the field 110. For example, in someembodiments, each of the plurality of drones may utilize a same type ofsensor, but scan different portions of the field 110. In otherembodiments, one or more of the plurality of drones may utilize a sensorthat is different from the other drones. In yet other embodiments, oneor more of the plurality of drones may perform a scan at one heightabove the ground, while the other drones perform scans at one or moreother heights. Again, the use of different sensors or scan at differentheights can separate the analysis into identifying areas of possibleobjects and separately identifying the objects.

In various embodiments, the sensor data may be modified or manipulatedprior to analyzing the data for objects. For example, such modificationsmay include stitching together and/or overlaying of one or more images,sets of data from one or more sensors, and the like. For example, wherea drone 105 generates a plurality of images of a field 110, theplurality of images can be stitched together to generate a single largercontiguous image. As described in more detail herein, these images maybe further pre-processed and manipulated prior to employing one or moreartificial neural networks to identify the objects.

The process of identifying objects and determining their locations canbe performed in real time, or near real time, as the drone 105 isscanning the field 110, or it can be performed after the drone 105 hascompleted the scan of the field 110 (post-processing of the sensordata). In some embodiments, the post processing of the sensor data canbe automatically performed when the drone 105 has completed its scan orwhen the drone 105 has established a wired or wireless connection with aprocessing server (e.g., object-detection server 602 in FIG. 6A), or thepost processing can be perform in response to a user manually initiatingthe processing.

After the drone 105 has completed its scan of the filed 110 and one ormore objects are identified and located, an object-collection system(not illustrated) (e.g., object collection system 300 in FIGS. 3A-3B)may be employed to pick up or otherwise collect the one or more objectsfrom the field 110, which is described in more detail below. In someembodiments, the object-collection system may be employed after anobject is identified and while the drone 105 is continuing to scan thefield. For example, the drone 105 may perform object identification andlocation determination while it is scanning the field 110. The drone 105can then transmit the object locations to the object-collection systemas they are being located. The object-collection system can then attemptto locate and pick up the objects.

In some embodiments, the object-collection system can provide feedbackto the drone 105 to indicate whether an object identified by the drone105 is picked up, missed, or is not an object at all (e.g., by analyzinghigher resolution images captured closer to the ground). The drone 105can use this information to re-scan an area, update its imagerecognition techniques, etc. In this way, the drone 105 and theobject-collection system coordinate the real-time (or near real-time)scanning and collection of objects. In other embodiments, the drone 105may transmit the captured images to another computing device, such asthe mobile user computer, to perform the object identification andlocation determination. This other computing device can then coordinatethe scanning of the target geographical area by the drone 105 and thecollection of objects by the object-collection system.

Although FIG. 1A illustrates the use of an aerial drone to scan thefield 110, embodiments are not so limited. In other embodiments, aground-based image-collection system may be employed to scan the field110. For example, a tractor may be configured with a sensor array toscan the field 110 while the tractor is performing other tasks, such asseeding, spraying, etc. As another example, an autonomous rover may beemployed to perform the first scan of the field 110. Once the scan iscomplete, the tractor, rover, or other object-collection system may bedeployed to collect the objects.

FIG. 1B is another example illustration of a drone 105 analyzing a field110 to identify and map objects 150 in accordance with embodimentsdescribed herein. In general, a scanning map, flight plan, or travelplan, is downloaded or otherwise installed on the drone 105. The flightplan identifies a route and altitude in which the drone 105 is to flyover the field 110. The drone 105 begins flight from a launch pad 132.In various embodiments the flight plan may identify specific GPScoordinates in which the drone 105 is to fly. In other embodiments, theflight plan may specify coordinates relative to the launch pad, whichmay also include a height above takeoff 134.

As illustrated, the drone 105 captures a first image at position 1. Atthis position, the drone captures a first image of image location 144 a,which is centered at the drone location 140 a and has a viewing angle136 a. The drone 105 may continue to capture additional images inaccordance to its flight plan. At some later point in time, the dronecaptures a second image of image location 144 b and has a viewing angle136 b. However, due to wind, flight characteristics, or otherenvironmental elements, the drone 105 may have tilted at the time theimage was captured such that the image location 144 b is not centered atthe drone location 140 b, but rather some distance away, which isfurther illustrated in FIG. 1D. Various embodiments described hereinutilize the avionic telemetry data of the drone 105 to determine anactual image position, and thus determines and actual location of object150.

To accurately employ one or more artificial neural networks to identifyobject 150, the image quality should be of a high enough resolution sothat the artificial neural networks can detect object features, whilealso encompassing a sufficient amount of ground space. Therefore, adesirable pixel-to-ground distance ratio should be determined.Unfortunately, due to undulations and imperfections in the field 110,the drone's height above the ground can fluctuate. For example, atposition 1, the drone 105 is at a first height above ground 138 a, andat position 2, the drone 105 is at a second height above ground 138 b.In this illustrated example, the drone is closer to the ground atposition 2 than at position 1. Many drones maintain their altitude basedon a height above takeoff 134, which may be more or less than thedrone's actual height above the ground at a current location. Therefore,in various embodiments described herein, the drone 105 includes a sensorto determine the drone's height above ground 138, which can be used bythe drone to maintain an appropriate height above ground or it can beused to determine an actual image location, such as at position 2.

FIG. 1C is an example image portion 156 that includes a representationof an object 150 that was captured by a drone in accordance withembodiments described herein. As described herein, images captured by adrone or other image-collection system are stretched and manipulated toremove distortion caused by uneven terrain or a non-vertical angle ofcapture, which is further described herein. The resulting image is animage with a uniform distribution of pixels to ground distance. Invarious embodiments, an example desired pixel-to-distance ratio is 15pixels to approximately 101.6 mm (or approximately four inches).Accordingly, FIG. 1C illustrates an image portion 156 having a width 157a of 15 pixels and a height 157 b of 15 pixels. Both the height 157 band width 157 a have an approximate ground coverage of 101.6 mm (or fourinches).

FIG. 1C illustrates just one example of a pixel-to-distance ratio of adesired resolution in which features of an object 150 can be detected bya trained artificial neural network. If fewer pixels are utilized torepresent the same ground distance, then the image may not have enoughdetail to identify an object 150 in the image, which can result inmissed smaller sized objects. Conversely, if more pixels are utilized torepresent the same ground distance, then the smaller objects sizes canbe identified, but the image may cover too small of an area—resulting inmany more images being captured to cover the entire target geographicalarea, which utilizes additional computing resources to process theadditional images. Therefore, the pixel-to-distance ratio can bemodified based on the application of the methods and systems describedherein. In some embodiments, the pixel-to-distance ratio can be modifiedby changing the camera or sensors utilized to capture images of thetarget geographical area. In other embodiments, a capture height (e.g.,height above ground 138 in FIG. 1B) may be adjusted to change thepixel-to-distance ratio.

FIG. 1D is a top view example illustration 158 of drone location 140 bof a drone 105 relative to a location 144 b of a captured image 170 of afield 110 in accordance with embodiments described herein. As mentionedabove, wind or other flight characteristics of the drone 105 may resultin a camera on the drone 105 being in some position or angle other thanvertical to the ground. Accordingly, a GPS captured location 140 b ofthe drone 105 may be different from that actual image location 144 b,which in this illustration is off to the right of the drone location 140b. As described in more detail herein, avionic telemetry information ofthe drone and the drone's location 140 b at a time of image capture canbe utilized to determine the actual image location 144 b of the image170. Once the location 144 b of the image is determined, a position ofobject 150 in the image 170 can be translated to an actual position ofthe object 150 within the target geographical area.

FIGS. 2A-2C are example illustrations of overlaying images to identifythe same object in multiple images in accordance with embodimentsdescribed herein. Example 200A illustrates three images—Image_1,Image_2, and Image_3 referenced as images 202 a, 202 b, and 202 c,respectively (collectively images 202)—of a target geographical area.Each image 202 is captured at a different location 210 and at adifferent orientation 204 and includes an object 206. Whether the object206 identified in each image 202 is in fact the same object or aredifferent objects can be determined based on the object position, imageposition, and orientation of the images.

For example, image 202 a is captured at orientation 204 a and includesobject 206 a; image 202 b is captured at orientation 204 b and includesobject 206 b; and image 202 c is captured at orientation 204 c andincludes object 206 c. Embodiments described herein are employed todetermine a center location 210 of image 202 in the target geographicalarea. For example, Image_1 (202 a) is centered at location 210 a,Image_2 (202 b) is centered at location 210 b, and Image_3 (202 c) iscentered at location 210 c. When images 202 are overlaid on each otherbased on their center location 210 and orientation 204, as shown in FIG.2C, then it can be determined that object 206 a in Image_1 (202 a) isthe same object as object 206 c in Image_2 (202 c), but is differentfrom object 206 b in Image_2 (202 b). In various embodiments, a pixellocation of each object 206 within the corresponding image 202, alongwith the physical location 210 of the images, can be used to determinewhether the objects are the same or different, which is described inmore detail below in conjunction with FIG. 11.

FIGS. 3A-3B are example illustrations of various embodiments of anobject object-collection system in accordance with embodiments describedherein.

FIG. 3A illustrates one example embodiment of an object-collectionsystem 300 that includes a vehicle 355 with a bucket 360 that includesan object-collector apparatus 365 disposed at a front-end of the bucket360. A sensing array 370 is shown disposed at a top end of the bucket360. As described herein, in some embodiments, a generatedobject-pick-up path or route can be used to direct a user in driving thesystem 300 proximate to one or more previously identified objects suchthat the object-collector apparatus 365 can automatically pick up theobjects and deposit the objects in the bucket 360. In other words, thegenerated route of travel can be used for rough positioning of theobject-collector apparatus 365 on the bucket 360 so that theobject-collector apparatus 365 can automatically pick up one or moreobjects at a given waypoint along the route of travel.

Although FIG. 3A illustrates an example embodiment of a system 300comprising a loader with a bucket 360, it should be clear that otherembodiments can comprise any suitable vehicle of various suitableconfigurations. For example, other embodiments can include a truck,all-terrain vehicle (ATV), tractor, dump truck, a specializedproprietary vehicle, and the like. Additionally, while the example ofFIG. 3A illustrates an example of a vehicle 355 having a cab for a humanoperator, various examples can include manned or unmanned vehicles,which may or may not be configured for use by a human operator. Forexample, in some embodiments, the vehicle 355 can be operated by a humanuser sitting in the cab of the vehicle 355 or can be configured forautonomous use without a human user sitting in the cab or other locationon the vehicle 355.

Also, while the example of FIG. 3A and other examples herein illustratea bucket 360 associated with an object-collector apparatus 365, wherepicked objects are deposited in the bucket 360 by the object-collectorapparatus 365, further examples can use various other suitablecontainers for objects and/or locations for an object-collectorapparatus 365 to be disposed. Accordingly, in some examples, a bucket360 can be absent from a system 300.

In embodiments having a bucket 360, such a bucket 360 can comprise astandard bucket with elements such as an object-collector apparatus 365and/or sensor array 370 being removably coupled to the standard bucketor can comprise a specialized bucket 360 having elements such as anobject-collector apparatus 365 and/or sensor array 370 integrallydisposed thereon. In some examples, it can be desirable to convert acommercially available front-loader into a system 300 by coupling anobject-collector apparatus 365 and/or sensor array 370 to the bucket 360of the commercially available front-loader. In other examples, aspecialized object picking bucket 360 can be coupled with a vehicle 355configured for a bucket 360.

Additionally, while the example of FIG. 3A illustrates anobject-collector apparatus 365 coupled to a bottom leading edge of thebucket 360, in further examples, an object-collector apparatus 365 canbe coupled in any suitable location on a bucket 360 or other locationabout the vehicle 355 or system 300. Similarly, the example of theobject-collector apparatus 365 of FIG. 3A should not be construed asbeing limiting on the wide variety of suitable object-collectorapparatuses 365 that can be associated with a system 300. Furthernon-limiting examples of some suitable object-collector apparatuses 365are discussed in more detail herein.

Also, as discussed herein, the sensor array 370 can be coupled in anysuitable location on a bucket 360 or other location about the vehicle355 or system 300. Additionally, the sensor array 370 can comprise oneor more suitable sensors, including a camera, RADAR, LIDAR, SONAR,positioning device (e.g., GPS, compass and the like), a microphone, andthe like. A camera can include any suitable type of camera, including avisible light camera, infrared camera, ultraviolet camera, thermographiccamera, and the like. Additionally, in some examples, a system 300 caninclude a plurality of sensor arrays 370, which can be disposed on anysuitable location of a vehicle 355 or external to the vehicle 355 (e.g.,disposed on a drone that follows the vehicle 355).

FIG. 3B illustrates another example embodiment of an object-collectionsystem 300. In this illustrated example, a camera 312 captures images ofa ground area 324 in front of the object-collection system 300. As theobject-collection system 300 travels along the field 110, the cameracaptures a plurality of images. The images are analyzed using one ormore artificial neural networks to identify objects 310, which theobject-collection system 300 can pick up using object-collectorapparatus 322. The object-collector apparatus 322 may be an embodimentof object-collector apparatus 626 in FIG. 6A, which is described in moredetail below. Briefly, the object-collector apparatus 322 is configuredto pick up an object 310 when the object-collection system 300approaches the object 310 without picking up or greatly disturbing theground.

In various embodiments, the object-collection system 300 also includes auser computer or display 320 (e.g., mobile user computer device 620).The display 320 presents one or more images or screens to a user of theobject-collection system 300, which may be captured by camera 312. Oneexample embodiment of such a display is shown in FIG. 5.

FIGS. 4A-4B are example illustrations of images captured by an objectobject-collection system to identify, track, and pick up objects inaccordance with embodiments described herein. As described herein, theobject-collection system may include one or more cameras or othersensors that capture images of a direction of travel of theobject-collection system. Image 400 is one such example of an imagecaptured from the object-collection system.

As described herein, the object-collection system may include a catcherswath in which the object-collection system can pick up an object whenthe object-collection system is within pick-up range of the object. Thiscatcher swath is illustrated by a bottom 406 a width and a top 406 bwidth of the image 400. Because the camera is angled towards the ground,as shown in FIG. 3B, the top catcher swath 406 b is further away fromthe object-collection system and is thus narrower than the bottomcatcher swatch 406 a, which is closer to the object-collection system.Accordingly, the image 400 is distorted with non-uniformpixel-to-distance ratios, where the ground at the top 406 b has a denserdistribution of pixels per ground distance unit than the bottom 406 a.The change of pixel-to-distance ratio from the bottom 406 a of the image400 to the top 406 b of the image 400 is also shown by the left side 406c and right side 406 d of the image 400.

Image 400 also includes objects 402 and paddles 404. As described inmore detail herein, the paddles 404 are one embodiment of a pickerassembly for picking up and collecting the objects 402. One or moreimage recognition techniques may be employed on image 400 to identifythe objects 402 and a position of the paddles 404 relative to theobjects 402.

In various embodiments, before the objects 402 and paddles 404 areidentified in the image 400, the image 400 is stretched and transformedsuch that there is an even and uniform pixel distribution horizontallyleft 406 c to right 406 d and from top 406 b to bottom 406 a, which isillustrated as image 410. In this way, objects 402 can be tracked acrossmultiple images 410 without distortion caused by capture angle of thecamera relative to the ground. With the objects 402 being trackedindependent of image distortion, movement of the paddles 404 can also betracked and aligned with the objects 402 for collection withoutdistortion.

FIG. 4B illustrates one embodiment of tracking an object 422 throughoutmultiple image frames 420. It should be recognized that a plurality ofimage frames are captured and the object 422 is identified and itslocation in the image noted to track movement of the object 422throughout the images. For ease of discussion, embodiments are oftendescribed as movement of the object being tracked. However, the objectitself is not moving. Rather, the perception of the object's location ismoving in the images relative to the object-collection system as theobject-collection system approaches the object. In some embodiments,however, the object could be physically moving.

In the illustrated example image 420, a location of the object 422 isidentified in the image 420 at three different times, T1, T2, and T3.T1, T2, and T3 may be consecutive image frames, or they may at someother image frame or time interval. As the object-collection systemapproaches the object 422, the paddles 404 are also moved to align withthe object 422. In various embodiments, an artificial neural network maybe employed to identify a position of the paddles 404 in the image 420.In other embodiments, an electromechanical measurement system may beused to determine a position of the paddles.

In some embodiments, the paddles 404 may be vertically lowered from astorage height to a pick-up height. The storage height is a positionwhere the paddles 404 are held to avoid contact the ground. In someembodiments, the storage height is configured to maintain the paddles404 in the image 420, while allowing the paddles to move horizontally toalign with the object 422. The pick-up height is a position where thepaddles 404 contact the ground or are otherwise positioned to pick-upthe rock, which may be within or outside the image 420.

Because it takes time for the paddles to move from the storage height tothe pick-up height, a trigger line 426 is employed to activate movementof the paddles 404 from the storage height to the pick-up height. Theposition of the trigger line 426 may be determined based on a speed inwhich the object-collection system is approaching the object 422—thefaster the object-collection system is approaching the object 422, thehigher in the image 420 the trigger line 426 is positioned. Accordingly,a speed at which the object-collection system appears to be approachingthe object 422 is determined by tracking movement of the object 422across the image 420. Unfortunately, this speed may vary over time dueto user speed adjustments, camera movement, or other visual aspectchanges. For example, if the object-collection system drives over alarge rock, the capture angle of the camera itself can also change,which results in the object being in a different location in subsequentimages. But this change of object location is due to the camera movementcaused by the rock and not actually because of the object-collectionsystem getting closer to the object.

Although an image-defined trigger line is utilized to activate themovement of the paddles 404 to pick up an object, embodiments are not solimited. Rather, in some embodiments, one or more other dynamic controlmethods based on kinematics or visual surveying may also be utilized toactivate the paddles 404. For example, GPS data on the object-collectionsystem can be analyzed over time to identify a speed and direction inwhich the object-collection system is moving, which may be utilized todetermine when to pick up an object based on the previously determinedlocation of the object (e.g., by determining the object location fromimages, as described herein).

FIG. 4C is an example illustration of an image utilizing specific imageareas to identify the speed of the object-collection system inaccordance with embodiments described herein. As discussed above, image440 includes a catcher swath area 446. In some embodiments, the image440 may also include a visible area 444 outside of the catcher swatharea 446.

To determine the approach speed of the object-collection system to theobject, a plurality of image tracking portions 442 are identified orpositioned on the image 440. The image tracking portions 442 may bepositioned anywhere on the image 440, but may be more accurate whenpositioned near a bottom of the image within the catcher swath area 446.One or more feature characteristics are determined or identified in eachimage tracking portion 442. These feature characteristics are uniquefeatures to image 440 within the boundary defined by the image trackingportions 442, which may include variations in dirt color, edges orridged features on the ground, etc. These unique features are trackedover multiple image frames using optical flow tracking. A vertical pixellocation of each unique feature is captured for a select number of imageframes, such as 20 frames. The vertical pixel locations per frame foreach image tracking portion may be plotted on a graph, such as shown inFIG. 4D.

FIG. 4D is an example illustration of a graph 460 showing the trackedvertical pixel location 462 of a tracked feature across multiple imageframes for a particular image tracking portion. Linear regressiontechniques may be employed on vertical pixel location 462 to identify aspeed of the tracked feature, which is the number of pixels the trackedfeature moved per frame for the particular image tracking portion. Aseparate speed may be determined for each of the plurality of imagetracking portions in this way, which are then averaged to generate thecurrent speed at which the object-collection system is approaching theobject.

Returning to FIG. 4B, with the current speed and a known time of howlong it takes the paddles 404 to move from the storage height to thepick-up height, a vertical pixel location of the trigger line 426 can bedetermined.

FIG. 5 is an example illustration of a display 500 presented to a driverof the object-collection system in accordance with embodiments describedherein. In various embodiments, display 500 includes a first screen 502and a second screen 504. In some embodiments, the first screen 502 maybe on one display device and the second screen 504 may be on a second,separate display device. In other embodiments, the first screen 502 maybe on a first portion of a display device and the second screen 504 maybe on a second portion of the same display device. In at least oneembodiment, the display device presenting the first screen 502, thesecond screen 504, or both may be on a mobile user computer device(e.g., mobile user computer device 130 in FIG. 1 or mobile user computerdevice 620 FIG. 6A).

In the illustrated example, the first screen 502 is a current view fromthe camera on the object-collection system, as discussed above. In thisexample, the first screen 502 shows three objects being approached bythe object-collection system (i.e., the object-collection system isapproaching the objects. In various embodiments, a tracking area 510 maybe employed in which target objects 518 in the tracking area 510 aretracked using one or more neural networks, as described herein. In someembodiments, these neural networks may be trained similar to those usedto identify the object in images captured from an image-collectionvehicle.

In some embodiments, bounding boxes may be added to the displayed imageto illustrate which objects are being tracked via trained neuralnetworks. If an object is not being tracked then the user may be enabledto interact with the first screen 502 to add a bounding box around acurrently non-tracked object. Once the user-added bounding box is added,the system may use optical flow tracking or other image trackingtechniques to track movement of the object across multiple image frames.In other embodiments, the user may be enabled to interact with the firstscreen 502 to de-select an object from being tracked. For example, ifthe user determines that the object is not a pick-up eligible object,then the user can input that information into the system so that it nolonger tracks the object and does not attempt to pick-up the object. Insome embodiments, a flag or other indicator may be added to the objectinformation to indicate that the object was not picked up.

The first screen 502 may also display other information to the user. Forexample, the first screen 502 can include a speed-adjustment indicator508 to notify the user to speed up or slow down the movement speed ofthe object-collection system. This speed indicator may be based on acurrent speed of the object-collection system and a desired or optimalcollection speed. For example, in an area with multiple objects, theuser may be instructed to slow down to give the system time to pick upeach object. Conversely, in an area with very few objects, the user maybe instructed to speed up.

In other embodiments, the first screen 502 may display an approachindicator 506. The approach indicator 506 may instruct the user to movethe object-collection system to the right or to the left to improve theobject-collection system's ability to pick up an object. This approachindicator 506 may also be used to instruct the user on which way to turnthe object-collection system to keep the object-collection system on thepick-up path determined to pick up the objects.

Although these indicators are described as being displayed to a user,embodiments are not so limited. In other embodiments, the indicatorinstructions may be provided to an autonomous control computer that isconfigured to automatically control the operation of theobject-collection system with little or no input from the user. Alsoillustrated in FIG. 5 is a second screen 504. In this illustration, thesecond screen 504 displays a map or aerial representation of the targetgeographical area. The second screen 504 may illustrate the target orpick-up path 514 in which the object-collection system is to travel topick up the target objects 518. The width of the target path 514 may bedisplayed to show the swath width 512 of the object-collection system520 (which may be an embodiment of object-collection system 618 in FIG.6A).

In some embodiments, the second screen 504 may display oversized ornon-pick-up eligible objects 516. In other embodiments, the secondscreen 504 may remove the visual representation of these objects. Forexample, the second screen 504 may include a size adjustment input 522.The size adjustment input 522 enables the user to select different sizedobjects that are to be picked up by the object-collection system. If theuser clicks on the “plus” icon, then the object-collection system mayidentify, track, and pick up larger objects. If the user clicks on the“minus” icon, then the object-collection system may identify, track, andpick up smaller objects. In some embodiments, the objects 518 beingshown in the second screen (and tracked in the first screen 502) maychange based on the user's selection.

FIG. 6A illustrates a context diagram of a system 600A for scanning atarget geographical area, identifying objects in that area, andemploying an object-collection system to pick up the objects inaccordance with embodiments described herein. System 600A includes anobject-detection server 602, a mobile user computer device 620, animage-collection vehicle 616, an object-collection system 618, atarget-area database 604, and an object-information database 606, whichare operably connected and communicate via a communication network 610.

The image-collection vehicle 616 is a vehicle or system that includes asensor array 622 for collecting images or other sensor data of a targetgeographical area. The image-collection vehicle 616 may be a manned orunmanned aerial vehicle, such as a helicopter, airplane, glider, kite,balloon, satellite, or other aerial flying device, which may includedrone 105 in FIGS. 1A-1B. Although some embodiments describe theimage-collection vehicle 616 as being an aerial-image-collectionvehicle, embodiments are not so limited. In other embodiments, theimage-collection vehicle 616 may be substituted, replaced, or used inconjunction with a ground-image-collection vehicle or system. Theground-image-collection vehicle may be a suitable manned or unmannedground vehicle, such as a truck, tractor, ATV, or the like, may also beutilized to scan the a target geographical area for objects. In yetother embodiments, one or more hand-held user devices, such as asmartphone, tablet computer, theodolite, camera, etc. may be utilized.Accordingly, while some examples discussed herein include a drone beingused to scan and identify objects in a target geographical area, otherexamples can include other types of suitable devices. Theaerial-image-collection vehicle and the ground-image-collection vehiclemay be generally referred to as an image-collection vehicle orimage-collection system.

The image-collection vehicle 616 includes one or more sensors in asensor array 622. Such sensors can include a camera, RAdio Detection AndRanging (RADAR), LIght Detection And Ranging (LIDAR), SOund NavigationAnd Ranging (SONAR), and the like. A camera can include any suitabletype of camera, including a visible light camera, infrared camera,ultraviolet camera, thermographic camera, and the like. The sensor array622 is utilized to capture images and data of a target geographicalarea.

The object-detection server 602 is a computing device that receives datafrom the image-collection vehicle 616 and employs one or more trainedartificial neural networks to identify objects in the images captured bythe image-collection vehicle 616. The object-detection server 602 storesinformation regarding the identified objects, such as a location andapproximate size, in the object-information database 606. In someembodiments, the object-detection server 602 may also receive data fromthe object-collection system 618 and employ one or more trainedartificial neural networks to identify and track objects as theobject-collection system 618 approaches the objects for pick up.

The mobile user computer 620 is a computing device that presentsinformation to a user via a graphical user interface. In variousembodiments, the mobile user computer 620 is an embodiment of mobileuser computer device 125 in FIG. 1A. The mobile user computer 620 can beany suitable computing device, including a tablet computer, asmartphone, laptop computer, desktop computer, wearable computer,vehicle computer, gaming device, television, and the like.

The object-collection system 618 is a system configured to maneuveracross a target geographical area and pick up objects, as describedherein. For example, system 300 in FIGS. 3A-3B may be an embodiment ofobject-collection system 618.

The object-collection system 618 can include a processor or othercontroller (not illustrated) that is configured to control and/orreceive data from sensor array 624 and object-collector apparatus 626 totrack and pick up objects, as described herein.

Additionally, in various embodiments, the controller can be configuredto control and/or receive data from other components of theobject-collection system, such as a vehicle (e.g., vehicle 155 in FIG.3A) or portions thereof. For example, in some embodiments, thecontroller can be configured to drive an autonomous vehicle and drive anobject-collector apparatus 626 of the autonomous vehicle based at leastin part on data from the autonomous vehicle, the sensor array 624 andobject-collector apparatus 626. In other examples, the controller can belimited to control of the object-collector apparatus 626 based at leastin part on data from the object-collector apparatus 626, sensor array624, and the like, as described in more detail herein.

While the object-collection system 618 is described as having acontroller to track objects using the sensor array 624 and controlmovement of the object-collection system 618, embodiments are not solimited. In some embodiments, such functionality of the controller maybe performed by or in conjunction with the object-detection server 602.

Accordingly, in various embodiments, one or more of theobject-collection system 618, mobile user computer device 620, orobject-detection server 602, or a combination thereof, can perform someor all steps of methods, functions, or operations described herein.

The object-detection server 602 can comprise various suitable systems ofone or more virtual or non-virtual computing devices. In variousexamples, the object-detection server 602 can be remote from the mobileuser computer device 620, object-collection system 618, sensor array624, and object-collector apparatus 626. The communication network 610can comprise any suitable wired or wireless communication networkincluding a Wi-Fi network, Bluetooth network, cellular network, theInternet, a local area network (LAN), a wide area network (WAN), or thelike.

The example system 600A of FIG. 6A is for illustrative purposes andshould not be construed to be limiting. For example, in someembodiments, a plurality of image-collection vehicles 616 may beemployed to scan a target geographical area. In other embodiments, aplurality of mobile user computer devices 620 can be employed tocollectively present information to one or more users, as describedherein. In yet other embodiments, a plurality of object-collectionsystems 618 may be employed to collectively collect objects from thetarget geographical area.

FIG. 6B shows a system diagram that describes one implementation ofcomputing systems for implementing embodiments described herein. Similarto FIG. 6A, system 600B includes object-detection server 602, mobileuser computer device 620, image-collection vehicle 616,object-collection system 618, target-area database 604, andobject-information database 606.

Regarding the object-detection server 602, one or more special-purposecomputing systems may be used to implement object detection server 602to train and utilize one or more artificial neural networks (or otherimage recognition techniques) to identify objects in images (or othersensor data) of a target geographical area, as described herein.Accordingly, various embodiments described herein may be implemented insoftware, hardware, firmware, or in some combination thereof.

The object detection server 602 includes memory 630, one or more centralprocessing units (CPUs) 644, I/O interfaces 648, display 646, othercomputer-readable media 650, and network connections 652. The objectdetection server 602 may include other computing components that are notshown for ease of illustration.

Memory 630 may include one or more various types of non-volatile and/orvolatile storage technologies. Examples of memory 630 may include, butare not limited to, flash memory, hard disk drives, optical drives,solid-state drives, various types of random access memory (RAM), varioustypes of read-only memory (ROM), other computer-readable storage media(also referred to as processor-readable storage media), or the like, orany combination thereof. Memory 630 is utilized to store information,including computer-readable instructions that are utilized by CPU 644 toperform actions and embodiments described herein.

For example, memory 630 may have stored thereon object-management system632. Object-management system 632 includes object-identification module634 and object-collection module 636 to employ embodiments describedherein. For example, the object-identification module 634 trains one ormore artificial neural networks that are utilized to identify, classify,and determine a location of objects in a target geographical area basedon sensor data collected from the image-collection vehicle 616. Theobject-collection module 636 trains one or more artificial neuralnetworks that are utilized to identify, classify, and track a locationof objects to allow an object-collection system 618 to pick up theobjects.

The object-identification module 634, the object-collection module 636,or both, may interact with other computing devices, such as theimage-collection vehicle 616 to collect sensor data; and mobile usercomputer device 620 to receive boundary information or display arepresentation of object locations; and object-collection system 618 topick up the objects; and object-information database 606 to store thelocations of objects; and target-area database 604 to store informationregarding the target geographical area, as described herein. Althoughillustrated separately and on a same computing device, the functionalityof the object-identification module 634 and the object-collection module636 may be performed by a single module, or by a plurality of modules onone or more computing devices. Memory 630 may also store other programsand data to perform other actions associated with the operation ofobject detection server 602.

Network connections 652 are configured to communicate with othercomputing devices, such as mobile user computer device 620,image-collection vehicle 616, object-collection system 618, target-areadatabase 604, object-information database 606, or other devices notillustrated in this figure, via one or more communication networks 610.In various embodiments, the network connections 652 include transmittersand receivers (not illustrated) to send and receive data as describedherein. Display 646 is configured to provide content to a display devicefor presentation of the graphical user interface to a user. In someembodiments, display 646 includes the display device, such as atelevision, monitor, projector, or other display device. In otherembodiments, display 646 is an interface that communicates with adisplay device. I/O interfaces 648 may include a keyboard, audiointerfaces, video interfaces, or the like. Other computer-readable media650 may include other types of stationary or removable computer-readablemedia, such as removable flash drives, external hard drives, or thelike.

Mobile user computer device 620 is a computing device that receivesinformation from the object-detection server 602 to present a graphicaluser interface to a user to enable the user to input boundaryinformation or to display a representation of the target geographicalarea and the location of identified objects, as described herein. Themobile user computer device 620 includes a memory, CPU, I/O interfaces,display, other computer-readable media, and network connections, similarto object-detection server 602, but are not illustrated in FIG. 6B forsimplicity. Accordingly, the mobile user computer device 620 may storecomputer instructions that when executed by a processor cause theprocessor to perform actions described herein.

Briefly, mobile user computer device 620 may have stored thereonobject-display system 664 and an object-management system 670. Theobject-display system 664 may include an object-identification-displaymodule 666 and an object-collection-display module 668. Theobject-identification-display module 666 presents a graphical userinterface that displays a visual representation of a target geographicalarea and the locations of objects identified in the target geographicalarea. The object-collection-display module 668 presents a graphical userinterface that displays images of the target geographical area as theobject-collection system 618 approaches objects to be picked up and arepresentation of the location of the object-collection system 618relative to the object locations. Although illustrated separately and ona same computing device, the functionality of theobject-identification-display module 666 and object-collection-displaymodule 668 may be performed by a single module, or by a plurality ofmodules on one or more computing devices.

The object-management system 670 may include object-identificationmodule 672 or object-collection module 674, which may performembodiments of object-identification module 634 and object-collectionmodule 636, respectively, of the object-detection sever 602. In thisway, the mobile user computer device 620 may perform at least some ofthe identification, collection, and tracking functionality locally.

The image-collection vehicle 616 captures image or sensor data of atarget geographical area, as described herein. The image-collectionvehicle 616 includes a memory, CPU, I/O interfaces, display, othercomputer-readable media, and network connections, similar toobject-detection server 602, but are not illustrated in FIG. 6B forsimplicity. Accordingly, the image-collection vehicle 616 may storecomputer instructions that when executed by a processor cause theprocessor to perform actions described herein. The image-collectionvehicle 616 may include object-identification module 672, which mayperform embodiments of object-identification module 634 of theobject-detection sever 602. In this way, the image-collection vehicle616 may perform at least some of the identification functionalitylocally.

The image-collection vehicle 616 captures image or sensor data of atarget geographical area, as described herein. The image-collectionvehicle 616 includes a memory, CPU, I/O interfaces, display, othercomputer-readable media, and network connections, similar toobject-detection server 602, but are not illustrated in FIG. 6B forsimplicity. Accordingly, the image-collection vehicle 616 may storecomputer instructions that when executed by a processor cause theprocessor to perform actions described herein. The image-collectionvehicle 616 may include object-identification module 680, which mayperform embodiments of object-identification module 634 of theobject-detection sever 602. In this way, the image-collection vehicle616 may perform at least some of the identification functionalitylocally.

The object-collection system 618 captures image or sensor data of atarget objects to be picked up and picks up those objects, as describedherein. The object-collection system 618 includes a memory, CPU, I/Ointerfaces, display, other computer-readable media, and networkconnections, similar to object-detection server 602, but are notillustrated in FIG. 6B for simplicity. Accordingly, theobject-collection system 618 may store computer instructions that whenexecuted by a processor cause the processor to perform actions describedherein. The object-collection system 618 may include object-collectionmodule 682, which may perform embodiments of object-collection module636 of the object-detection sever 602. In this way, theobject-collection system 618 may perform at least some of the trackingand collection functionality locally.

The target-area database 604 may store information about one or moretarget geographical areas, including boundary information or exclusionzones. The object-information database 606 stores information aboutidentified objects, including a determined physical location of theidentified objects.

The operation of certain aspects will now be described with respect toFIGS. 7-15. Processes 700 s, 800 s, 900 s, 1000 s, 1100 s, 1200 s, 1300s, 1400 s, and 1500 s described in conjunction with FIGS. 7-15,respectively, may be implemented by or executed via circuitry or on oneor more, or a combinations of, computing devices, such asobject-detection server 602, object-collection system 618, mobile usercomputer device 620, or image-collection vehicle 616. For example,object-detection server 602 may perform all or parts of processes 800 s,900 s, 1000 s, 1100 s, 1200 s, or 1300 s; object-collection system 618may perform all or parts of process 1500 s; mobile user computer device620 may perform all or parts of processes 700 s, 800 s, 900 s, 1000 s,or 1100 s; and image-collection vehicle 616 may perform all or parts ofprocesses 800 s, 900 s, 1000 s, or 1100 s. These examples are not to beconstrued as limiting; rather, various combinations of computing devicesmay be perform various elements of the various processes.

FIG. 7 illustrates a logical flow diagram showing one embodiment of aprocess 700 s for instructing an image-collection vehicle to scan atarget geographical area in accordance with embodiments describedherein. Process 700 s begins, after a start block, at block 702 s, wherean order to scan or map a target geographical area for objects isreceived. In various embodiments, a user may input, select, or otherwiserequest a scan via a user computing device (e.g., mobile user computer620).

In some embodiments, the user may also input or select a type ofimage-collection vehicle (e.g., image-collection vehicle 616 in FIG. 6Aor drone 105 in FIG. 1A) or the type of sensors to be utilized duringthe scan. For example, the user may indicate that the image-collectionvehicle is to utilize both thermal imaging and visual spectrum imaging.As another example, the user may indicate that the image-collectionvehicle is to execute a first scan at a first height above the ground toidentify possible areas of objects and a second scan at a second height(e.g., lower than the first height) above the ground to identify objectswithin the possible areas of objects. The use of different scan heightsmay be to reduce the total amount of processing being performed, toimprove accuracy for areas of interest, accommodate for differentresolutions, utilize different sensors that may operate more efficientlyat different heights, or other factors.

In yet other embodiments, one or more scanning passes may be utilized,which may be at a same height or at different heights. For example, insome embodiments, an image-collection vehicle may be employed toidentify areas-of-interest within the target geographical area aspossibly containing one or more objects. At some later time, aground-image-collection vehicle (e.g., a tractor or autonomous rover)may be employed to re-scan the areas-of-interest.

In at least one embodiment, the user may also input various parametersor conditions associated with the target geographical area. For example,the user can input an expected type of object to be found in thegeographical area (e.g., a rock or specific type of rock, a specifictype of fruit, a human, etc.), a type of crop being planted at thetarget geographical area, a status of the crop (e.g., not planted,planted but not sprouted, plant clearing the ground by 2 cm, etc.), atime of year, current or expected weather at the time when theimage-collection vehicle is to scan the target geographical area, anexpected type or amount of non-cultivated vegetation (e.g., a type ordensity of weeds, possibility of trees or shrubs, etc.), or otherconditions that may alter, change, or effect the ability to identifyobjects.

Process 700 s proceeds to block 704 s, where boundary information forthe geographical target area is identified. In various embodiments, auser can select or input the boundary information, such as by entering alegal property address, entering GPS coordinates, utilizing a graphicaluser interface to draw or place borders on an image, or otherinformation that identifies the target geographical area.

In various embodiments, the user may also input one or more exclusionzones. In some embodiments, the user can utilize a graphical userinterface to draw, identify, define, or place borders of the exclusionarea on an image. The exclusion zones further define areas not to bescanned by the image-collection vehicle. Accordingly, the boundaryinformation may indicate an area to be scanned or it may indicate anoutside or exterior boundary and one or more internal boundaries thatare not to be scanned.

Process 700 s continues at block 706 s, where a flight plan (or travelplan) is generated based on the boundary information. The flight planmay be any suitable “auto-pilot” or preprogramed instructions designedfor the image-collection vehicle to maneuver over the targetgeographical area. In various embodiments, the flight plan includes aplurality of waypoints within the target geographic area in which theimage-collection vehicle is to follow. The flight plan may also includea designated height in which the image-collection vehicle is to fly.Such height may be a height above takeoff, or it may be a set heightabove the ground. As described herein, the flight height may be setbased on the desired image resolution of the images captured by theimage-collection vehicle. Process 700 s proceeds next at block 708 s,where the image-collection vehicle is instructed to scan the targetgeographical area based on the flight plan. In some embodiments, theflight plan is uploaded or otherwise installed on the image-collectionvehicle. In some embodiments, the flight plan may be modified ormanipulated mid-scan based on missed areas, changes, in weather, or forother reasons.

After block 708 s, process 700 s terminates or otherwise returns to acalling process to perform other actions.

FIG. 8 illustrates a logical flow diagram showing one embodiment of aprocess for identifying and mapping objects in a target geographicalarea in accordance with embodiments described herein. Process 800 sbegins, after a start block, at block 802 s, where a mapping of thetarget geographical area is generated. This mapping may be the scanningor capturing of sensor data of the target geographical area. Forexample, as discussed herein, in some embodiments, a drone 105 can scana field 110.

Process 800 s proceeds to block 804 s, where objects are identified inthe mapping (images or scan data) of the target geographical area. Invarious embodiments, computer vision or artificial intelligence, or somecombination thereof, can be used in analyzing portions of the mapping toidentify objects within the target geographical area. For example, invarious embodiments an identification algorithm (or artificial neuralnetwork) can be trained using training mappings, images, or other datawhere users have specifically identified “objects” or “not objects.” Infurther examples, training mappings can be generated based on generatedmappings of previous target geographical areas and on data obtainedwhile picking up objects in the previous target geographical area. Insome embodiments, the identification of an object in the mappings mayalso include a confidence score for such an identification.

In various embodiments, identifying objects can also include identifyingone or more characteristics of identified objects. Such characteristicscan include, but are not limited to, an estimated object volume orvolume range; an estimated object mass or mass range; an estimatedobject height above the ground or height range; an object shape type;and the like. For example, object shape types can include, spherical,ovoid, planar, elongated, irregular, polished, rough, and the like. Invarious embodiments, the size or volume of the identified objects may bedetermined based on a number of image pixels associated with theobjects, which is described in more detail herein.

Moreover, as described in more detail herein, a location of the objectsin the target geographical area may also be determined.

Process 800 s continues at block 806 s, where the identified objects areclassified. Classification of identified objects can be based on one ormore other characteristics, which may be performed during theidentification process. These classification characteristics can includea classification of “pick-up eligible” or “not pick-up eligible,” aspecific type of object, etc.

For example, in various embodiments, it can be desirable to remove anyobjects from a field that may interfere with activities in the field,such as tilling, planting, irrigation, pruning, weeding, spraying,harvesting, and the like. In such examples, objects can havecharacteristics such that they would not interfere with certainactivities in the field 110 and therefore need not be picked up; canhave characteristics such that they would interfere with certainactivities in the field 110 and therefore should be picked up, ifpossible; can have characteristics such that they can be picked up by anobject-collection system (e.g., object-collection system 618); can havecharacteristics such that they cannot be picked up by the object pickingsystem; and the like. Accordingly, where a given object would interferewith certain field activities and was capable of being picked up by theobject-collection system, the object can be classified as “pick-upeligible.” On the other hand, if a given object is incapable of beingpicked up by the object-collection system or has characteristics whereit would not interfere with certain field activities, then the objectcan be classified as “not pick-up eligible.”

In at least one embodiment, a user may set or select the characteristicsthat define a pick-up eligible object from a not pick-up eligibleobjects. For example, a user can utilize a graphical user interface toset a target threshold size such that object larger than the thresholdsize are not pick-up eligible and objects smaller than the thresholdsize are pick-up eligible. In some embodiments, the user may set asecond, lower threshold size such that an object that is smaller thanthe second threshold is not pick-up eligible. In at least oneembodiment, the graphical user interface presented to the user maydisplay representations of all identified objects, pick-up eligibleobjects (e.g., those that meet the user's threshold size), not pick-upeligible, etc., which may be based on user preferences or selections.

In some other embodiments, classification of the identified objects mayalso include indicating a type of object. For example, in someembodiments, the mapping (e.g., image or sensor data) of the targetgeographical area may be analyzed by each of plurality of artificialneural networks, where each artificial neural network is trained toidentify different types of objects. Accordingly, the identified objectsmay be classified as rocks, human, animal, or some other trainedclassification.

Process 800 s proceeds next to block 808 s, where one or moreobject-picking waypoints are generated based on the mapping andclassification of objects. In some embodiments, the object-pickingwaypoints may be determined as the location of the objects. For example,the location of objects classified as “pick-up eligible” can be includedas an object-picking waypoint. Additionally, in some examples, anobject-picking waypoint can comprise a plurality of objects. Forexample, where a plurality of objects are clustered together (e.g., suchthat the plurality of objects can be picked up by the object-collectionsystem without further moving the object-collection system), then agiven object-picking-waypoint can comprise a plurality of objects.

In other embodiments, the object-picking waypoints may be various pointsor locations within the target geographical area (which may be at theobject locations or not) in which the object-collection system canmaneuver to run over or come across the object for pick up. These objectwaypoints can be utilized as data points or travel waypoints in apick-up path or route to instruct or guide an object-collection systemto the objects in the target geographical area for pick up, as describedherein.

After block 808 s, process 800 s terminates or returns to a callingprocess to perform other actions.

FIG. 9 illustrates a logical flow diagram showing one embodiment of aprocess 900 s for identifying objects and guiding an object-collectionsystem to pick up the identified objects in a target geographical areain accordance with embodiments described herein. Process 900 s begins,after a start block, at block 902 s, where a first plurality of imagesof a target geographical area are captured. As described herein, animage-collection vehicle may capture images using one or more cameras orsensors. In some embodiments, the first plurality of images are visualspectrum images. In other embodiments, the first plurality of images arecaptured using other sensors (e.g., thermal images, infrared images,etc.).

The first plurality of images may be captured at preset or selected timeor distance intervals such that they are consecutive or adjacent imagesthat include a partially overlapping area of the target geographicalarea. In some embodiments, image processing techniques may be employedto determine if there are non-overlapping images such that portions ofthe target geographical area are missed by the scanning process. In atleast one such embodiments, the image-collection vehicle may beinstructed to re-traverse over the target geographical area to captureimages of the missed area.

Although embodiments described herein generally refer to the firstplurality of images as being captured from the air, embodiments are notso limited. In other embodiments, one or more cameras or sensors held bya person or mounted on a ground-operated vehicle (e.g., a tractor,all-terrain vehicle, truck, etc.) may also be used to capture the firstplurality of images.

Process 900 s proceeds to block 904 s, where one or more objects areidentified in the first plurality of images. In various embodiments, thefirst plurality of images are analyzed by one or more artificial neuralnetworks that are trained to identify (and classify) objects, which isdescribed in more detail herein. Briefly, for example, one artificialneural network may be utilized on one subset of the first plurality ofimage and a second artificial neural network may be utilized on a secondsubset of the plurality of images. These two artificial neural networksmay be trained to identify different objects, trained to identifyobjects in different conditions, etc. The subsets of images may begroups of images based on location (e.g., search zones), groups ofimages based on conditions, alternatingly captured images, etc.

In various embodiments, it may be desirable to have each image include auniform relationship between the number of image pixels and thecorresponding ground distance, e.g., 15 pixels corresponds toapproximately 101.6 mm (four inches). This uniformity may improve theefficiency of the artificial neural networks and the identifications ofobject. In some instances, however, one or more of the first pluralityof images may not have such uniformity due to inconsistencies in theslope of the ground, tilt of the image-collection vehicle, or othervisual effects. Accordingly, in some embodiments, the first plurality ofimages may be modified to remove such distortion. An example embodimentof one process for removing the distortion and identifying a location ofobjects in an image is described below in conjunction with FIG. 10.

Although embodiments described herein refer to the analysis of images toidentify object, embodiments are not limited to the analysis of wholeimages. Rather, in some embodiments, each captured image may besegmented into a plurality of tiles. Each tile can then be processedthrough an artificial neural network, or other vison recognitiontechniques or algorithms, to identify objects. In some embodiments, thesize of each tile may be selected based on the processing size of theneural network. Moreover, the selected tiles may overlap a selectedpercentage or number of pixels.

Process 900 s continues at block 906 s, where object information for theidentified objects is determined. The object information of eachidentified object may include an approximate physical location of thatcorresponding object, an approximate size of the corresponding object, atype of the corresponding object, a pickability classification of thecorresponding object, etc., such as described elsewhere herein.

Briefly, the physical location of an object may be determined based onthe pixel location of the object within an image and the location of theimage, which may be based on the physical location and telemetry data ofthe image-collection vehicle when the image was captured. The size ofthe object may be determined, for example, by analyzing the number ofpixels that include or make up the object in the images. In someembodiments, duplicate identified object may be determined and removedor ignored, such as shown in FIGS. 2A-2C and discussed in conjunctionwith FIG. 11.

In various embodiments, the object information for each identifiedobject is stored in a database, such as object-information database 606in FIG. 6A. As described herein, the object information can be used topresent a graphical user interface to a user showing a representation ofthe location (or size or pickability) of each identified object. Theobject information can also be used to generate an object densityheat-map, as well as routing information in which to guide anobject-collection system to pick up the objects.

Process 900 s proceeds next to block 908 s, where an object-collectionsystem is guided over the target geographical toward the identifiedobjects based on the object information. In some embodiments, the objectwaypoints described above may be utilized to guide the object-collectionsystem.

In various embodiments, a pick-up path algorithm is utilized todetermine a path across the target geographical area in which traverseto pick up the objects in a most efficient or quickest manner. Asdescribed herein, the pick-up path may be a best or optimal path to pickup the objects, a suboptimal or close to optimal path, and may bedetermined utilizing one or more clustering or path planning algorithms.In at least one embodiment, a user can input or select one or morepick-up parameters, which indicate which objects are to be picked up.This information can be used to label identified objects as being“tagged” for pick up or “ignored.” The object information for the taggedobject can then be utilized to determine the pick-up path of theobject-collection system.

In some embodiments, guiding the object-collection system along thepick-up path may include displaying a map or visual instructions to auser to manually drive or maneuver the object-collection system over thetarget geographical area towards the objects, such as illustrated inFIG. 5. In other embodiments, the pick-up path may include GPScoordinates or travel waypoints that can be used by an autonomousvehicle to travel across the target geographical area.

Process 900 s continues next at block 910 s, where a second plurality ofimages of the target geographic area are captured relative to theobject-collection system while the object-collection system is beingguided over the target geographical area. In various embodiments, thesecond plurality of images are captured from one or more cameras orsensors mounted on the object-collection system. For example, a visualspectrum camera can be mounted on the front of the object-collectionsystem and capture images in front of and in the travel path of theobject-collection system, such as illustrated in FIG. 4B. The secondplurality of images can be captured as the object-collection system istraversing over the target geographical area to monitor the ground infront of the object-collection system.

Process 900 s proceeds to block 912 s, where one or more target objectsare identified in the second plurality of images. In variousembodiments, block 912 s may employ embodiments similar to block 904 sto identify objects in the images. An object identified in an image ofthe second plurality of images may be referred to as the target object.In at least one embodiment, the artificial neural networks utilized inblock 912 s may be trained using different training data than used totrain the artificial neural networks utilized in block 904 s because ofthe different camera angles. In other embodiments, however, the sameartificial neural networks may be utilized.

When a target object is identified in an image of the second pluralityof images, the target object is tracked through multiple images as theobject-collection system approaches the identified object, which isillustrated in and discussed in conjunction with FIGS. 4A-4D.

Process 900 s continues at block 914 s, where the object-collectionsystem is instructed to pick up the target objects. In variousembodiments, instructing the object-collection system includes an objectcollector apparatus that moves to be in position with the target object,pick up the target object, and place the target object in a holding binfor removal from the target geographical area. Example systems andapparatuses for picking up objects are discussed in more detail below inconjunction with FIGS. 16-40.

Process 900 s proceeds next to decision block 916 s, where adetermination is made whether to continue to guide the object-collectionsystem over the target geographical area. As discussed herein, theobject-collection system may be guided by a pick-up path of multipletravel waypoints across the target geographical area. Accordingly, thedetermination of whether to continue to guide the object-collectionsystem may be based on additional travel waypoints in the pick-up path.In other embodiments, the determination may be based on whether thereare additional target objects in the second plurality of images. If theobject-collection system is guided further over the target geographicalarea, then process 900 s loops to block 908 s; otherwise, process 900 sterminates or otherwise returns to a calling process to perform furtheractions.

FIGS. 10A-10B illustrate a logical flow diagram showing one embodimentof a process 1000 s for modifying captured images to enableidentification of objects in accordance with embodiments describedherein. Process 1000 s begins, after a start block in FIG. 10A, at block1002 s, where an image of the target geographical area is captured. Invarious embodiments, block 1002 s may employ embodiments similar tothose described in conjunction with block 902 s in FIG. 9 to capture animage of a target geographical area in which to identify objects. Insome embodiments, process 1000 s may be performed on theimage-collection vehicle. In other embodiments, process 1000 s may beperformed by another computing device, such as the object-detectionserver 602 or mobile user computer device 620 in FIG. 6A, and thecaptured images may be received from the image-collection vehicle.

Process 1000 s proceeds to block 1004 s, where avionic telemetryinformation of the image-collection vehicle is captured during imagecapture. The avionic telemetry information may include GPS location ofthe image-collection vehicle, pitch of the image-collection vehicle,roll of the image-collection vehicle, yaw of the image-collectionvehicle, heading of the of the image-collection vehicle, inertialmeasurements, altitude, or other information indicating the positioningand movement of the-image-collection vehicle. As mentioned above,process 1000 s may be performed by another computing device, such as theobject-detection server 602 or mobile user computer device 620 in FIG.6A, and the avionic telemetry information may be received from theimage-collection vehicle.

Process 1000 s continues at block 1006 s, where a capture height of theimage-collection vehicle is determined. The capture height may also bereferred to as the ground sample distance (GSD). In various embodiments,a LIDAR or other sensor may be employed on the image-collection vehicleto determine the height of the image-collection vehicle above the groundat the time the image is captured. If the target geographical area isflat, then other sensor data, such as altitude or height above takeoffmay also be used to determine the height of the image-collection vehicleat the time of image capture.

Process 1000 s proceeds next to block 1008 s, where an image positionwithin the target geographical area is determined. In variousembodiments, the avionic telemetry information of the image-collectionvehicle and the capture height is utilized to determine a physicalposition of the image. For example, the roll, pitch, yaw, and heading ofthe image-collection vehicle can determine an orientation and angle ofcapture from the image-collection vehicle relative to the ground. Theorientation and angle of capture, height above ground, and GPS locationof the image-collection vehicle can be utilized, along withtrigonometric calculations, to determine an actual position of thecaptured image. This image position may be determined relative to acenter of image. In other embodiments, the image position may bedetermined for a particular corner of the captured image.

Process 1000 s continues next at block 1010 s, where a homographytransform is performed on the captured image. The homography transformstretches and converts the image into an image with uniform pixeldistribution per ground unit. The homography transform removes imagedistortion caused by variations in ground slope and shape and the angleof capture from the image-collection vehicle. In various embodiments, adesirable transformed image may result in 15 pixels equating toapproximately 101.6 mm (or approximately four inches) of grounddistance.

Process 1000 s proceeds to block 1012 s, where image recognition isperformed on the transformed image to identify one or more objects. Invarious embodiments, block 1012 s may employ embodiments similar toembodiments described in block 904 s in FIG. 9 to identify one or moreobjects in the transformed image.

Process 1000 s continues at block 1014 s in FIG. 10B, where a firstpixel location of each identified object in the transformed image isdetermined. The first pixel of a corresponding object may be amathematical center pixel of the corresponding object. For example,during object identification, a bounding box may be generated to enclosethe features used to identify the object. The center of the bounding boxmay be set as the first pixel location of the corresponding objectwithin the transformed image.

Process 1000 s proceed next to block 1016 s, where a reverse homographytransform is performed on each first pixel location to determine asecond pixel location of each identified object in the original image.The reverse homography transform converts each corresponding first pixellocation in the transformed image into a corresponding second pixellocation in the original image, which reverses the stretching and imagedistortion corrections performed in block 1010 s in FIG. 10A. In variousembodiments, each corresponding second pixel location may be referred toas a center position of each corresponding object in the originallycaptured image.

Process 1000 s continues next at block 1018 s, where a position of eachidentified objects is determined based on the corresponding second pixellocation and the determined image position. In various embodiments, adistance and orientation may be calculated between the determined imageposition and the corresponding second pixel location to determine a GPSor other physical location of the corresponding object.

Process 1000 s proceeds to block 1020 s, where the determined positionof the identified objects is stored. In some embodiments, the determinedposition of each identified object may be sent to and stored on a remotedatabase, such as object-information database.

Process 1000 s continues at decision block 1022 s, where a determinationis made whether another image is captured. In some embodiments, aplurality of images may be captured as the image-collection vehicle isscanning the target geographical area, as described herein. In someembodiments, process 1000 s may be performed as images are beingcaptured or received. In other embodiments, process 1000 s may beperformed post scan. If another image was captured, process 1000 s loopsto block 1002 s in FIG. 1A; otherwise, process 1000 s terminates orotherwise returns to a calling process to perform other actions.

FIG. 11 illustrates a logical flow diagram showing one embodiment of aprocess 1100 s for removing duplicative identifications of objects inaccordance with embodiments described herein. Process 1100 s begins,after a start block, at block 1102 s, where a first identified objecthaving a first determined position is selected. As described herein, aplurality of objects may be identified in an image captured by theimage-collection vehicle, with a physical location of each identifiedobject also being determined. The first identified object may beselected from the plurality of identified objects.

Process 1100 s proceeds to block 1104 s, where a second identifiedobject having a second determined position is selected. In variousembodiments, the second identified object may be a second identifiedobject from the plurality of identified objects, similar to block 1102s.

Process 1100 s continues at block 1106 s, where an orientation of afirst image that contains the first identified object is determined. Invarious embodiments, the orientation of the first image may be based onthe heading of the image-collection vehicle when the first image wascaptured by the image-collection vehicle, such as when the avionictelemetry information is captured at block 1004 s in FIG. 10A.

Process 1100 s proceeds next to block 1108 s, where an orientation of asecond image that contains the second identified object is determined.In various embodiments, the orientation of the second image may be basedon the heading of the image-collection vehicle when the second image wascaptured by the image-collection vehicle, similar to block 1106 s. Insome embodiments, blocks 1106 s and 1008 s may be optional and may notbe performed when the duplication determination at decision block 1114 sis based solely on a distance between the identified objects.

Process 1100 s continues next at block 1110 s, where the first object isadded to a list of identified objects in the target geographical area.In various embodiments, the list of identified objects may be a list ofobject that are positively identified as being unique objects in thetarget geographical area.

Process 1100 s proceeds to decision block 1114 s, where a determinationis made whether the second object is a duplicate of the first objectbased on the image orientations and the determined positions of thefirst and second identified objects. In various embodiments, one or moredistance thresholds may be utilized to determine if the secondidentified object is a duplicate of the first identified object.

In various embodiments, if a distance between the first determinedposition of the first identified object exceeds a first thresholddistance from the second determined position of the second identifiedobject, then the second identified object is not a duplicate of thefirst identified object. Conversely, if the distance between the firstdetermined position of the first identified object is below a secondthreshold distance from the second determined position of the secondidentified object, then the second identified object is a duplicate ofthe first identified object.

If, however, the distance between the first determined position of thefirst identified object is below the first threshold distance andexceeds the second threshold distance, then the image orientations maybe utilized to determine if the second identified object is a duplicateof the first identified object. For example, assume the first and secondimages are taken side-by-side, with the second image being on the rightside of the first image, and both images have a same orientation. If thesecond determined position of the second identified object is closer tothe second image than the first determined position of the firstidentified object, then the second identified object may not be aduplicate of the first identified object. Conversely, if the seconddetermined position of the second identified object is closer to thefirst image than the first determined position of the first identifiedobject, then the second identified object may be a duplicate of thefirst identified object.

In some embodiments, if a determination of whether the second identifiedobject is a duplicate of the first identified object cannot bedetermined, such as if the distance between the identified objects isbetween the two thresholds, then a flag may be stored with the firstidentified object to indicate a possible cluster of multiple objects.

In some other embodiments, pattern or shape matching may also beutilized, alone or in combination with object locations, to determine ifthe second identified object is a duplicate of the first identifiedobject. For example, in some embodiments a first shape of the firstidentified object and a second shape of the second identified objectsare determined using one or more image recognition techniques. The firstshape is rotated or aligned with the second shape based on theorientations of the first and second images. If the aligned shapes firstshape resembles (e.g., matching borders) the second shape within athreshold amount, then the second identified object is determined to bea duplicate of the first identified object.

If the second identified object is a duplicate, process 1100 s flows toblock 1118 s where the second object is ignored; otherwise, process 1100s flows to block 1116 s where the second object is added to the list ofidentified objects.

After block 1118 s and block 1116 s, process 1100 s terminates orotherwise returns to a calling process to perform other actions.

FIG. 12 illustrates a logical flow diagram showing one embodiment of aprocess 1200 s for employing multiple artificial neural network toselect a preferred neural network in accordance with embodimentsdescribed herein. Although embodiments are described with respect toemploying artificial neural networks, embodiments are not so limited andother computer vision algorithms or technique may be used, as discussedabove.

Process 1200 s begins, after a start block, at block 1202 s, where afirst neural network for a first set of conditions is trained based on afirst set of images containing known objects.

In various embodiments, the first set of images is selected as eachincluding the first set of conditions and at least one object. The firstset of conditions may include one or more conditions. Examples, of suchconditions include, but are not limited to, a type of object (e.g., arock or specific type of rock, a specific type of fruit, a human, etc.),a type of crop, a status of the crop (e.g., not planted, planted but notsprouted, plant clearing the ground by 2 cm, etc.), a time of year,specific weather, an amount of non-cultivated vegetation (e.g., a typeor density of weeds, possibility of trees or shrubs, etc.), or otherconditions that may alter, change, or effect the ability to identifyobjects.

In various embodiment, the known object may have been previouslyidentified and marked in the first set of images. For example, humansmay be tasked with identifying and marking known objects in the firstset of images. Once the known objects are marked, the first set ofimages are input to a learning process of an artificial neural network,such as a deep neural network or other machine learning algorithm forprocessing images, resulting in the first neural network. The resultingtrained first neural network may include one or more weights orparameters files or datasets that implicitly represent characteristicsof the marked known objects.

Process 1200 s proceeds to block 1204 s, where a second neural networkfor a second set of conditions is trained based on a second set ofimages containing known objects. In various embodiments, block 1204 smay employ embodiments of block 1202 s to train the second neuralnetwork, but with a different set of images for a different set ofconditions. The second set of conditions may be a completely differentset of conditions from the first set of conditions, or the second set ofconditions may include at least one different condition from the firstset of conditions (but may share some of the same conditions).

Process 1200 s continues at block 1206 s, where a set of target imagesin which to identify objects is received. The target images areassociated with a third set of conditions that include at least onecondition that is different from the first and second sets ofconditions. In some embodiments, the set of target images may bereceived during a scan of a target geographical area, such as at block902 s in FIG. 9 or at block 910 s in FIG. 9. Accordingly, the third setof conditions may be those of the specific target geographical area (ora search zone within the target geographical area) during the scan. Inother embodiments, the set of target images may be associated with adesirable set of conditions for a future scan. For example, the firstset of conditions may be for a field of wheat that is two inches tall,the second set of conditions may be for a field with no visible crop,and the third set of conditions may be for a field of corn that is oneinch tall.

Process 1200 s proceeds next to block 1208 s, where the target imagesare analyzed using the first and second neural networks to identifyobjects. In various embodiments, block 1208 s may employ embodiments ofblock 904 s in FIG. 9 to identify objects using the first and secondneural networks.

In some embodiments, all target images are first analyzed using thefirst neural network, and all target images are subsequently re-analyzedusing the second set of neural networks. In other embodiments, thetarget images may be alternatingly analyzed using the first and secondneural networks. For example a first image from the target images may beanalyzed using the first neural network, a second image from the targetimages may be analyzed using the second neural network, a third imagefrom the target images may be analyzed using the first neural network, afourth image from the target images may be analyzed using the secondneural network, and so on. Although this example alternates the analysisfor every other image, embodiments are not so limited. Rather, theanalysis may alternate every n number of images, where n may be selectedby an administrator or user.

Process 1200 s continues next at block 1210 s, where an accuracy of thefirst neural network with respect to the third set of conditions isdetermined. In some embodiments, a human may be utilized to check eachidentified object to determine if each identified object is a correctpositive identification. In other embodiments, a human may check everyimage (or spot check select images) for objects not identified by thefirst neural network. In yet other embodiments, the accuracy of thefirst neural network may be based on an average or aggregate confidencefactor calculated from a confidence factor assigned to each identifiedobject by the first neural network.

Process 1200 s proceeds to block 1212 s, where an accuracy of the secondneural network with respect to the third set of conditions isdetermined. In various embodiments, block 1212 s may employ embodimentssimilar to those of block 1210 s to determine an accuracy of the secondneural network. In some other embodiments, the accuracy of the first andsecond neural networks may be determined by comparing the objectsidentified using the first neural network with the objects identifiedusing the second neural network. Differences between the identifiedobjects may then be checked by a human reviewer for accuracy.

Process 1200 s continues at decision block 1214 s, where a determinationis made whether the first neural network performed at a higher accuracythan the second neural network. In some embodiments, the higher accuracymay be the neural network that resulted in the higher number orpercentage of correct positive identifications of objects. In otherembodiments, the higher accuracy may be the neural network that resultedin the lower number or percentage of false positives or false negatives,or some combination thereof. In yet another embodiment, the higheraccuracy may be the neural network that resulted in a highest aggregateconfidence factor for the identified objects. If the first neuralnetwork performed at a higher accuracy than the second neural network,then process 1200 s flows to block 1216 s; otherwise, process 1200 sflows to block 1218 s.

At block 1216 s, the first neural network is selected as a preferredneural network for the third set of conditions.

At block 1218 s, the second neural network is selected as the preferredneural network for the third set of conditions.

After block 1216 s and after block 1218 s, process 1200 s terminates orotherwise returns to a calling process to perform other actions.

FIG. 13 illustrates a logical flow diagram showing one embodiment of aprocess 1300 s for predicting and selecting an artificial neural networkto use based on specific conditions of the target geographical area andthe expected objects in accordance with embodiments described herein. Insome embodiments, process 1300 s may be performed subsequent to process1200 s in FIG. 12.

Process 1300 s begins, after a start block, at block 1302 s, where asecond set of target images in which to identify objects is received.The second set of target images are associated with a fourth set ofconditions. In various embodiments, block 1302 s may employ embodimentssimilar to block 1206 s in FIG. 12 to receive the second set of targetimages.

Process 1300 s proceeds to block 1304 s, where a first closeness factoris determined between the fourth set of conditions and the first set ofconditions. In some embodiments, the first closeness factor may be anumber (or percentage) of conditions that are the same or shared betweenthe first and fourth sets of conditions. In other embodiments, one ormore conditions may have an assigned weight based on how much thecorresponding condition effects the results of the neural networkanalysis. For example, the height of the crop may have more of an impactthan the weather. In this example, the crop height condition may have ahigher weight compared to the weather condition. Accordingly, thecloseness factor may be determined based on the number or percentage ofsimilar conditions but modified based on the assigned weights.

Process 1300 s continues at block 1306 s, where a second closenessfactor is determined between the fourth set of conditions and the secondset of conditions. In various embodiments, block 1306 s may employembodiments similar to block 1304 s to determine the closeness factorbetween the fourth and second sets of conditions.

Process 1300 s proceeds next to block 1308 s, where a third closenessfactor is determined between the fourth set of conditions and the thirdset of conditions. In various embodiments, block 1308 s may employembodiments similar to block 1304 s to determine the closeness factorbetween the fourth and third sets of conditions.

Process 1300 s continues next at decision block 1310 s, where adetermination is made whether the first, second, or third closenessfactors is the highest. In various embodiments, the first, second, andthird closeness factors may be compared to determine the highestcloseness factor. If the fourth set of conditions matches one of thefirst, second, or third sets of conditions, then the correspondinglymatched set of conditions has a highest closeness factor. If the firstcloseness factor is highest, then process 1300 s flows to block 1312 swhere the second set of target images are analyzed using the firstneural network. If the second closeness factor is highest, then process1300 s flows to block 1314 s where the second set of target images areanalyzed using the second neural network. And if the third closenessfactor is highest, then process 1300 s flows to block 1316 s where thesecond set of target images are analyzed using the preferred neuralnetwork selected in FIG. 12.

After blocks 1312 s, 1314 s, and 1316 s, process 1300 s terminates orotherwise returns to a calling process to perform other actions.

FIG. 14 illustrates a logical flow diagram showing one embodiment of aprocess 1400 s for selecting an artificial neural network to employ onzones of a target geographical area to identify objects in accordancewith embodiments described herein. Process 1400 s begins, after a startblock, at block 1402 s, where a target geographical area is segmentedinto a plurality of search zones. In some embodiments, the search zonesmay be a user selected or predetermined size. In other embodiments, thesearch zones may be based on an even distribution of the targetgeographical area.

Process 1400 s proceeds to block 1404 s, where images for each searchzone are captured. In various embodiments, block 1404 s may employembodiments similar to block 902 s of FIG. 9 to capture images of thetarget geographical area with the images grouped based on theirassociated zone.

Process 1400 s continues at block 1406 s, where images for each searchzone are analyzed using multiple neural networks to identify objects. Invarious embodiments, block 1406 s may employ embodiments similar toblock 1208 s in FIG. 12 to analyze the captured images using multipleneural networks. In some embodiments, the images for each search zonemay be further divided into sub zones that are analyzed using theplurality of neural networks.

Process 1400 s proceeds next to block 1408 s, where an accuracy for eachneural network is determined for each zone (or sub zone). In variousembodiments, block 1408 s may employ embodiments similar to block 1210 sto determine an accuracy of each neural network, but with the accuracyof each neural network being separately determined for each search zone.Accordingly, each corresponding search zone includes a separate accuracyfor each of the plurality of neural networks.

Process 1400 s continues next at block 1410 s, where the search zonesare grouped based on the neural network accuracy. In variousembodiments, a highest accuracy neural network for each search zone isselected for that corresponding zone. The search zones that share thesame highest accuracy neural network are grouped together for thatassociated neural network. In other embodiments, for each neuralnetwork, a select number of highest accuracy search zones for thatassociated neural network are selected. In this way, the search zonesare grouped based on the neural network that identified objects thebest.

Process 1400 s proceeds to block 1412 s, where a condition classifier istrained for each group of search zones and the associated neuralnetwork. Accordingly, the conditions for each group of search zones aredetermined and the best neural network for those conditions isdetermined.

Process 1400 s continues at block 1414 s, where the trained conditionclassifier and associated neural network are employed to select a neuralnetwork to apply to zones in a new target geographical area. In variousembodiments, the new target geographical area is segmented into searchzones. The trained condition classifier is then employed for eachcorresponding search zone to identify the conditions of thecorresponding search zone. The neural network associated with theidentified conditions is then utilized to identify objects in thatcorresponding search zone, as described herein. The use of search zonesenables the system to detect changes in the conditions across a targetgeographical area and modify which neural network is being utilized toidentify objects as the conditions change.

FIG. 15 illustrates a logical flow diagram showing one embodiment of aprocess 1500 s for guiding an object object-collection system to pick uppreviously identified objects in a target geographical area inaccordance with embodiments described herein. Process 1500 s begins,after a start block, at block 1502 s, where an object waypoint isselected. For example, as discussed herein, a mapping or scanned imagesof a field or target geographical area can be analyzed to identifyobjects and determine their location. As discussed herein, anobject-picking waypoint can be selected based at least in part on thelocation of one or more of the objects identified in the targetgeographical area. In various embodiments, the selected waypoint may beselected from a pick-up path that is determined as a preferred oroptimal pack for collecting identified objects in the targetgeographical area. In some embodiments, the pick-up path may be updatedor modified as the object-collection system is picking up objects basedon a number of new objects identified by a user or previously identifiedobjects being de-selected by the user, a number or percentage ofsuccessful collections of objects, etc.

Process 1500 s proceeds at block 1504 s, where directions to the objectwaypoint are provided. For example, in an embodiment where a user drivesthe object-collection system (e.g., object-collection system 606 in FIG.6A), the user can be presented with directions for driving or guidingthe object-collection system to the selected waypoint. In at least oneembodiment, a current location of the object-collection system may bedetermined, such as via GPS coordinates of the object-collection system.The directions for driving the object-collection system may then bedetermined from the current location to the object waypoint.

In various embodiments, the directions may be provided to the user via amobile user device, such as mobile user computer device 620 in FIG. 6A.The directions may be visual, audible, or tactile. For example, in someembodiments, directions or an indication of which direction theobject-collection system is to move toward an object may be displayed tothe user. This display may include a map of the target geographicalarea, including the location of the object-collection system or objectpicking assembly, or both, relative to the identified objects orselected waypoint, such as illustrated in FIG. 5.

Although some examples relate to a human user driving object-collectionsystem to one or more object waypoints along a pick-up path, someexamples can include an automated system or vehicle that automaticallytravels to one or more object waypoints based on the current location ofthe object-collection system and the next pick-up waypoint. In someexamples, providing directions to the object waypoint may includeproviding automatic travel instructions to a motor-control system thatautonomously controls the object-collection system to the location ofthe next target object. Process 1500 s continues at decision block 1506s, where a determination is made whether the end effector orobject-collector assembly is within effective range of a target object.In various embodiments, determining whether the end effector is withinan effective range of one or more objects at the selected objectwaypoint can be based on data from one or more camera, such as sensorarray 624 in FIG. 6A. For example, one or more of GPS data, visual datafrom a camera, distance data from one or more sensors, direction datafrom a compass, and the like can be used to determine whether the endeffector is within an effective range of one or more objects at theselected object waypoint.

In one example, location data and direction data (e.g., GPS and compassdata) can be used to determine that the end effector is within aneffective range of one or more objects at the selected object waypointand that the end effector is at an operative orientation relative to theone or more objects at the waypoint such that the end effector canengage and pick up the one or more objects. In other words, with a rangeof motion, robotic kinematics and/or degrees of freedom of an objectpicking assembly being known, a determination of whether the objectpicking assembly is within an effective range of an object at theselected object waypoint can be determined based on a presumed, known ordetermined location of the object relative to the object pickingassembly.

In some embodiments, a visual system (e.g., a camera) or range findingsystem can identify the position of an object relative to object pickingassembly. For example, in various embodiments, a plurality of images arecaptured from one or more cameras or sensors on the object-collectionsystem, similar to block 910 s in FIG. 9. These images may be capturedin a direction of movement of the object-collection system along thepick-up path or toward the selected waypoint, or towards a direction ofcollection by the object picking assembly, such as described above inconjunction with FIGS. 4A-4D.

One or more target objects may be identified in the plurality of imagesbased on a dataset of known-object features, similar to what isdescribed above in conjunction with block 912 s in FIG. 9. The movementof the target objects may be tracked through the plurality of images.The distance, and approach speed, of the target object away from theobject picking assembly based on the tracked movement. In someembodiments, the approach speed of the object-collection system towardsthe target object may be determined based on a tracked movement speed offeature characteristics in a plurality of image tracking portions,similar to what is shown and described in conjunction with FIGS. 4A-4D.

If the end effector is within effective range of a target object, thenprocess 1500 s flows to block 1508 s; otherwise process 1500 s loops toblock 1504 s to continue to the object waypoint.

At block 1508 s, an indication that the end effector is within effectiverange of the target object is provided. In some embodiments, thisindication may be a visual or audible indicator to a user via agraphical user interface. In various embodiments, block 1508 s may beoptional and may not be performed.

Process 1500 s proceeds next to block 1510 s, where automated pick up ofthe target object by the end effector is initiated. In variousembodiments, automated pick up of an object can be initiated by a useror automatically by a computing system, and a control device can usefeedback data from the object picking assembly, the sensor array and/orposition data (e.g., associated with the object or portions of theobject picking assembly) to drive the object picking assembly to attemptto pick up the object.

Process 1500 s continues next at block 1512 s, where object pick up bythe end effector is completed or terminated. Completion or terminationof picking up the object may be completed or terminated based on whetherthe object can be successfully removed from the target geographical areato an object-holding unit of the object-collection system (e.g., thecavity of a bucket). For example, in some embodiments, the attempt topick up the object can be unsuccessful or terminate where the object isunable to be moved from the position on the target geographical area tothe object-holding unit of the object-picking system. Examples ofscenarios where object pick up is terminated may include the objectpicking assembly inappropriately engaging the object; the object is tooheavy to be lifted by the object picking assembly; the object is of ashape and/or size such that the object picking assembly cannotappropriately engage the object; the object-collection system cannotlocate the object such that the object picking assembly is unable toengage the object; the object picking assembly experiences an error;aborted by a user; and the like.

Process 1500 s proceeds to block 1514 s, where object status for theobject waypoint is updated. In some embodiments, where the objectwaypoint is not the determined location of an object, but rather atravel waypoint, the object status for of an object may be updated basedon a match between the GPS location of the object-collection system anda previously identified object.

An object status for the object that was the being picked up can beupdated in various suitable ways. For example, where the pick up of theobject is determined to be successful, the status of the object can bechanged to “picked up” and the subject object can be removed from thegraphical user interface display or representation of the targetgeographical area to indicate a successful pick up of the object.

Where the pick up of the object is not successful, the status of theobject can be changed to “unsuccessful pick up,” “pick-up error,” “notpossible to pick up,” “not an object,” “too heavy,” “incompatibleshape,” “eligible for new pick-up attempt,” “ineligible for new pick-upattempt,” “user inspection required,” “object position changed,” and thelike. For example, where data from the attempt to pick up the subjectobject indicates that the object may be substantially larger or heavierthan initially projected (e.g., a large buried object with a smallportion exposed on the surface of the ground), that status of the objectcan be updated to indicate that the object cannot be picked up due tobeing too large or unmovable, and the object can be indicated asineligible for another pick-up attempt. In another example, where anattempt to pick up the object indicates that the object is not an object(e.g., it is a dirt clod, stump, or the like), the status of the objectcan be updated accordingly.

Process 1500 s continues at decision block 1516 s, where a determinationis made whether an additional object waypoint is available. In variousembodiments, as described herein, a pick-up path may include a pluralityof object waypoints. If another object waypoint is available, process1500 s loops to block 1502 s to select another object waypoint;otherwise, process 1500 s terminates or otherwise returns to a callingprocess to perform other actions.

Turning now to FIGS. 16A-16C, which illustrate various exampleembodiments of object picking assembly 165. Object picking assembly 165can have various suitable forms and may be an embodiment of objectpicking assembly 365 in FIG. 3A. For example, FIGS. 16A, 16B and 16Cillustrate one example embodiment 165A of an object picking assembly 165that comprises a pair of travelers 560 that move along a rail 570, witheach traveler 560 respectively associated with a head assembly 580. Morespecifically, a first traveler 560A is coupled with a first headassembly 580A and a second traveler 560B is coupled with a second headassembly 580B. In various embodiments, the movement of the travelers 560along the rail 570 can be actuated in various suitable ways includingvia electric motors, and the like.

The head assemblies 580 comprise a web 581 with a pair of arms 582extending from the web 581. In this example, an internal arm 5821 andexternal arm 582E are shown extending from the web 581. An axel 584extends from the traveler 560 with the arms 582 and web 581 rotatablycoupled to the axel 584. A roller 586 is rotatably disposed betweendistal ends of the arms 582 opposing the web 581 and axel 584. Invarious embodiments, the rotation of the heads 580 and rotation of therollers 586 can be actuated in various suitable ways including viaelectric motors, and the like.

In various embodiments, the head assemblies 580 can be disposed at anangle relative to an axis of the travelers 560 and rail 570. Forexample, as shown in FIG. 16C, an axis H of the heads 580 can be at anangle θ relative to an axis TR of the travelers 560 and rail 570.However, in further examples, the angle of the heads 580 relative to thetravelers 560 and rail 570 can be any suitable angle.

The rollers 586 can have various suitable configurations. For example,as shown in FIGS. 16A-16C, in one embodiment 586A, the rollers 586 canbe generally cylindrical with a taper from the edges of the rollers 586toward a central portion of the rollers 586. In another embodiment 586Bas shown in FIGS. 17A-17C and 18A-18C, the rollers 586 can comprise aplurality plates 637 having teeth 638 about edges of the plates 637,with the plates 637 being disposed on a shaft 639. As shown in FIG. 17A,the rollers 586 can taper from the edges of the rollers 586 toward acentral portion of the rollers 586 with the teeth 638 being offsetbetween plates 638 to generate a spiral configuration of the teeth 638about the face of the rollers 586.

Turning to FIGS. 17B, 17C, 18A, 18B, and 18C, an example method ofpicking up an object 120 disposed in a field 110 via a second embodiment165B of an object picking assembly 165 is illustrated. As shown in FIGS.17B, 17C, 18A, 18B and 18C, the second embodiment 165B of the objectpicking assembly 165 can comprise a first and second head assembly 580A,580B disposed on a rail 570, which can be associated with anobject-collection system 618 (see e.g., FIG. 6A).

The head assemblies 580 can comprise at least one arm 582 extending froman axel 584 that is configured to rotate the one or more arms 582. Aroller 586 can be rotatably disposed at a distal end of the arm 582opposing the axel 584. In various embodiments, the rotation of the headassemblies 580 and rotation of the rollers 586 can be actuated invarious suitable ways including via electric motors, and the like.

Turning to FIG. 17B, an object 120 can be disposed in a field 110 andthe object picking assembly 165 can be positioned about the object 120such that the object 120 is between the head assemblies 580, includingbetween the respective arms 582 and rollers 586. As shown in FIG. 17B,the rollers 586 can be spun inward (i.e., the first roller 586A is spuncounter-clockwise and the second roller 586B is spun clockwise). Thehead assemblies 580 can be rotated inward toward the object 120, suchthat the rollers 586 engage the object 120 and lift the object 120 fromthe field 110 and between the arms 582 as shown in FIG. 17C. Asillustrated in FIGS. 17C, 18A and 18B, in some examples, the second headassembly 580B can remain in generally the same position as the firsthead assembly 580A is rotated upward to move the object 120 upwardtoward the axles 584. The object can then be deposited in a container(e.g., into a bucket 160 or other suitable container). The headassemblies 580 can then be rotated down as shown in FIG. 18C to put thehead assemblies 580 in position to engage another object 120.

Various embodiments of an object picking assembly 165 can operate as theexample embodiment 165B shown in FIGS. 17B, 17C and 18A-18C. Forexample, the embodiment 165A shown in FIGS. 16A-16C can operate to pickup objects 120 in a similar manner. Accordingly, in various embodiments,picking up objects 120 can comprise moving head assemblies 580 away fromeach other and/or toward each other via travelers 560 or other suitablestructures.

FIGS. 19A, 19B, 19C, 20A and 20B illustrate another example embodiment165C of an object picking assembly 165 that comprises a pair oftravelers 810 that move along a rail 820, with each traveler 810respectively coupled with a paddle assembly 830 via an arm 831. Morespecifically, a first traveler 810A is coupled with a first paddleassembly 830A and a second traveler 810B is coupled with a second paddleassembly 830B. In various embodiments, the movement of the travelers 810along the rail 820 can be actuated in various suitable ways includingvia electric motors, and the like. As shown in this example embodiment165C, the rail 820 can be coupled to a bucket 160 below a front edge 162of the bucket 160 and the paddle assemblies 830 can be configured topick up objects 120 and deposit the objects 120 into a cavity 161 of thebucket 160.

The arms 831 can be coupled to a respective paddle 832, with the armsconfigured to move the paddles 832 in various suitable ways, includingrotation, tilting, and the like. The paddles 832 can comprise a belt 836that is rotatably disposed about an external edge of the paddles 832with the belts 836 being configured to rotate clockwise and/orcounter-clockwise about the paddles 832. In various embodiments, therotation of the belts 836, movement of the arms 831 and/or movement ofthe travelers 810 can be actuated in various suitable ways including viaelectric motors, a pneumatic system, a hydraulic system, or the like.

The paddle assemblies 830 can be disposed relative to each other todefine a cavity 850, with the size and shape of the cavity 850configured to be changed by moving the paddles 832 relative to eachother. For example, the travelers 810 can move the paddle assemblies 830to widen and/or narrow the cavity 850 (see e.g., FIGS. 19B and 19C).Additionally, while FIG. 19A illustrates the paddles 832 defining cavity850 having a generally consistent width along a length of the cavity830, in various embodiments, the paddles 832 can be rotated to define acavity 850 that is narrower at a bottom end of the cavity 850 and/ornarrower at a top end of the cavity 850.

Also, while FIGS. 19A, 19B and 19C illustrate the paddles 832 disposedin a common plane, in various embodiments, the paddles 832 can beactuated (e.g., via the arms 831) to be disposed in different planes.For example, FIG. 20A illustrates a first and second paddle assembly830A, 830B disposed in a common place where axis ZA of the first paddleassembly 830A is disposed in the same plane as axis ZB of the secondpaddle assembly 830B. In contrast, FIG. 20B illustrates the first andsecond paddle assembly 830A, 830B disposed in different plane where axisZA of the first paddle assembly 830A is disposed in a different plane asaxis ZB of the second paddle assembly 830B.

In various embodiments, the paddle assemblies 830 can be used to pick upobjects 120 by positioning an object within the cavity 850 between thepaddles 832 such that the rotating belts 836 engage the object 120 andpull the object 120 from a base end of the cavity 850 to a top end ofthe cavity 850 such that the object 120 can be deposited in the cavity161 of the bucket 160. The size and shape of the cavity 850 can beconfigured to accommodate different sizes of objects 120 and tofacilitate picking up the objects 120 and depositing the objects 120into the bucket 160.

While FIGS. 19A-19C and 20A-20C illustrate one example of an objectpicking assembly 165 having paddle assemblies 830 coupled to a bucket160 via a rail 820 coupled to a front end of the bucket 160, furtherembodiments can include two or more paddle assemblies 830 coupled to abucket 160 in various suitable ways. For example, FIGS. 21A and 21Billustrate paddle assemblies coupled to a rail 820, with the rail 820being offset from and coupled to the bucket 160 via bars 1020 coupled tosidewalls of the bucket 160.

In another example embodiment 165D, as shown in FIGS. 22A, 22B, 22C, 23Aand 23B, an object picking assembly 165 can comprise a first and secondpaddle assembly 830 coupled to a single traveler 810, which can movealong a rail 820 that is suspended above and forward of the front edge162 of a bucket 160. As shown in this example embodiment 165D, a barassembly 1120 can extend from sidewalls of the bucket 160 to hold therail 820 above and forward of the front edge 162 of a bucket 160.

As shown in FIGS. 22B and 22C, in various embodiments the paddleassemblies 830 can be configured to be tilted to change the shape of thecavity 850 between the paddle assemblies 830. It should be clear thatsuch a capability can be present in any suitable embodiment (e.g., 165A,165B, 165C, and the like) and should not be construed to be limited tothis example embodiment 165D.

Also, while this example embodiment 165D illustrates a first and secondpaddle assembly 830 disposed on a single traveler 810, such that thefirst and second paddle assemblies travel together on the singletraveler 810, in some embodiments, the paddle assemblies 830 can bedisposed on separate respective travelers 810 (e.g., as shown inembodiments 165A, 165C, and the like). Similarly, further embodimentscan include a plurality of paddle assemblies 830, head assemblies 580,or the like, disposed on a single traveler 810, 560. Accordingly, itshould be clear that any suitable aspects of any example embodimentsherein can be applied to other example embodiments, and that thespecific configurations shown in the present example embodiments shouldnot be construed to be limiting on the numerous suitable configurationsthat are within the scope and spirit of the present invention. Also,while various embodiments discuss travelers 810, 560 as being configuredto move or travel along a rail 820, 570 or other structure, in someembodiments, travelers can be fixed in position and inoperable to moveor travel along a structure. Additionally, while various embodimentsillustrate an object picking assembly 165 comprising a pair of paddleassemblies 830, head assemblies 580, and the like, further embodimentscan include any suitable plurality of paddle assemblies 830, headassemblies 580 or other suitable elements. For example, some embodimentscan comprise three paddle assemblies 830 disposed on three separatetravelers 810 that can move along a rail 820. In such embodiments, thethree paddle assemblies 830 can define a first and second cavity 850between respective paddle assemblies 830 and objects 120 can be pickedup via the first and/or second cavities 850. Further embodiments caninclude a 4, 5, 10, 15, 25, 50 or other suitable number of paddleassemblies 830, head assemblies 580, and the like.

Object picking assemblies 165 can be configured in further suitable waysand can have other suitable structures for picking up or moving objects120. For example, FIG. 24A illustrates another example embodiment 165Eof an object picking assembly 165 that comprises a rail 1320 coupled toa front edge 162 of a bucket 160 via a pair of clamps 1325, such thatthe rail 1320 is offset from the front edge 162 of the bucket 160. Atine assembly 1330 can be coupled to the rail 1320 via a rail cuff 1310,and in some examples the rail cuff 1310 can be configured to rotateabout the rail 1320 and/or translate along the rail 1320 to actuate andmove the tine assembly 1330.

As shown in FIG. 24A, in one embodiment 1330A, a tine assembly 1330 cancomprise an arm 1332 that extends from the rail cuff 1310 to a tine head1334 that is coupled to a tine 1336 extends from the tine head 1334. Invarious examples, the tine head 1332 can be configured to rotate thetine 1336. Elements such as the rail cuff 1310 and/or tine head 1332 canbe actuated in various suitable ways including via an electric motor, apneumatic system, a hydraulic system, and the like.

As shown in FIG. 24B, in another embodiment 1330B, a tine assembly 1330can comprise a plurality of rail cuffs 1310 having one or more tines1336 extending from the respective rail cuffs 1310. For example, FIG.24B illustrates one embodiment 1330B where each rail cuff 1310 isassociated with a respective single tine 1336. In another exampleembodiment 1330C, as shown in FIGS. 25B and 26A, a given rail cuff 1310can be associated with a plurality of tines 1336 that define a tine unit1436 which can include cross bars 1437 that couple one or more of thetines 1336 of the tine unit 1436. In some examples, the rail cuff 1310can be configured to rotate about the rail 1320 and/or translate alongthe rail 1320 to actuate and move the tines 1336 and/or tine unit 1436.

For example, as shown in FIG. 26B, a tine unit 1436 can be coupled atthe front edge 162 of a bucket 160 via a clamp 1325, and the rail cuff1310 can be configured to rotate the tine unit 1436, which can bedesirable for scooping objects 120 from a field 110 and depositing themin the cavity 161 of the bucket 160 as discussed herein. In someexamples, a tine assembly 1330 can comprise a single rail 1320 that oneor more tines 1336 and/or tine units 1436 can travel along and/or rotateabout. However, in further embodiments, each of a plurality of tines1336 and/or tine units 1436 can travel along and/or rotate aboutseparate rails 1320.

Turning to FIGS. 27A-27C, another example embodiment of an objectpicking assembly 165F is illustrated that comprises a tine assembly 1630having a sleeve 1631 disposed about a rack 1632 and pinion 1634 with oneor more tines 1636 disposed at a front end of the rack 1632. The tineassembly 1630 can be coupled to the front edge 162 of a bucket 160 witha cylinder 1640 coupled to the bucket 160 and a front end of the rack1632.

By actuating the cylinder 1640, rack 1632 and/or pinion 1634, the tineassembly 1630 can be configured to extract objects 120 from a field 110and deposit the objects 120 into the cavity 161 of the bucket 160. Forexample, FIG. 27A illustrates the object picking assembly 165 in a firstconfiguration with the rack 1632 and cylinder 1640 in a first contractedconfiguration, and FIG. 27B illustrates the object picking assembly 165in a second configuration with the rack 1632 and cylinder 1640 in anextended configuration in preparation for engaging an object 120disposed in a field 110. As shown in FIG. 27C, the rack 1632 can thenassume a contracted configuration with the cylinder 1640 in an extendedconfiguration, which can lift the object 120 from the field 110 anddeposit the object 120 into the cavity 161 of the bucket 160.

In various embodiments, it can be desirable to configure an objectpicking assembly 165 to react to immovable objects that the objectpicking assembly 165 may encounter during operation. For example, FIGS.28A and 28B illustrate two example embodiments 165G, 165H of an objectpicking assembly 165 having a tine assembly 1730 configured to react tocontacting an immovable object, which in these examples is a largeobject 120 disposed within a field 110 with only a small portion of theobject 120 visible from the surface.

In the example of FIG. 28A the tine assembly 1730A can be configured ina forward “ready position” with the tine 1736 of the tine assembly 1730Aengaging the immovable object 120 and then assuming a “breakaway”position in response to engaging the immovable object 120 by rotatingbackward about an axle 1734. In some embodiments, the movement from the“ready position” to the “breakaway” position can be triggeredmechanically; based on data from one or more sensors (e.g., a forcesensor, camera, or the like). Additionally, rotation from the “readyposition” to the “breakaway” position can generated in various suitableways (e.g., via a return spring, a motor, a pneumatic system, ahydraulic system, or the like).

In the example of FIG. 28B, the tine assembly 1730B can be configured inan extended position with the tine 1736 of the tine assembly 1730Bengaging the immovable object 120 and then assuming a retracted positionin response to engaging the immovable object 120 by retracting the tine1736 away from the object 120. In some embodiments, the movement fromthe extended to retracted position can be triggered mechanically, basedon data from one or more sensors (e.g., a force sensor, camera, or thelike). Additionally, movement from the extended to retracted positioncan be generated in various suitable ways (e.g., via a return spring, amotor, a pneumatic system, a hydraulic system, or the like).

Turning to FIGS. 29A-29C, another example embodiment 1651 of an objectpicking assembly 165 is illustrated that includes a tine assembly 1830comprising a plurality of tines 1835 which spirally encircle and arecoupled to a rail 1820. In this example, the tines 1836 are shown havingdifferent lengths with the tines 1836 becoming successively longer fromthe edges of the tine assembly 1830 toward the center of the tineassembly 1830, which can generate a U-shape or V-shape. However, infurther examples, the tines 1836 can have various suitable lengths togenerate various suitable profiles for a tine assembly 1830 (e.g., flat,undulating, or the like). As illustrated in FIGS. 29B and 29C, the tineassembly 1830 can be configured to pick up an object 120 in a field 110by rotating the rail 1820 such that the object 120 engages and is heldwithin the tine assembly 1830, with further rotation enabling depositionof the object 120 to a container (e.g., a cavity 161 of a bucket 160).

Turning to FIGS. 30A and 30B, another example embodiment 165J of anobject picking assembly 165 is illustrated that includes a rim 1920 thatdefines slots 1921 via alternating flanges 1922, 1923 along the lengthof the rim 1920. Tines 1936 can be rotatably disposed within the slots1921 with one or more tine assemblies 1930 being defined by pairs oftines 1936A, 1936B coupled to and rotatably actuated by an actuator 1910disposed at the end of every other flange 1923. In some examples, thepair of tines 1936A, 1936B can be independently actuated or actuated inunison by the actuator 1910. As shown in FIG. 30B, one or more tines1936 can be configured to engage an object 120 in a field 110, which candirect the object 120 into a container (e.g., a cavity 161 of a bucket160).

In further examples, an object picking assembly 165 having a rim 1920and tines 1936 can be configured in various suitable ways. For example,in some embodiments, tines 1936 can be individually actuated or morethan two tines 1936 can be actuated as a unit. Additionally, tines 1936can be actuated in various suitable ways including via a motor,pneumatic system, a hydraulic system, or the like.

For example, FIGS. 31A, 31B, 31C, and 32B illustrate various exampleswhere tines or a tine assembly are actuated via one or more cylinder(e.g., a pneumatic or hydraulic cylinder). In the example embodiment165K of FIG. 31A, an object picking assembly 165 includes a tineassembly 2030 coupled to a front edge 162 of a bucket 160 via an axel2020, with one or more tines 2036 of the tine assembly 2030 beingactuated via a cylinder extending between the tine assembly 2030 and thebucket 160.

Additionally, the example of FIG. 31A illustrates a bucket 160 having adumping mechanism that includes a cylinder 1440 configured to actuate amovable base of the bucket 160, which can operate to dump objects 120disposed within the cavity 161 of the bucket 160. For example, FIG. 31Aillustrates the movable base in a closed position and FIG. 25Billustrates the movable base of a bucket 160 in an open position.

FIG. 31B illustrates another example embodiment 165L of an objectpicking assembly 165 coupled to a front edge 162 of a bucket 160 via aclamp architecture 1410 (see also FIG. 25A), with a cylinder 2040extending between the clamp architecture 1410 and one or more tines 2036of the tine assembly 2030. FIG. 31C illustrates another exampleembodiment 165M of an object picking assembly 165 where the tineassembly 2030 comprises an articulating arm having a first and secondlength 2031, 2032 rotatably coupled at a joint 2033, with thearticulating arm being actuated via a cylinder 2034 coupled to the firstand second length 2031, 2032.

FIG. 32A illustrates a further example embodiment 165N of an objectpicking assembly 165 coupled to or extending from a front edge 162 of abucket 160 including an arm 2120 and a tine 2136 coupled to andconfigured to translate along the length of the arm 2130 between adistal end 2121 of the arm 2120 and a base end 2122 of the arm 2120.More specifically, the same tine 2136 is shown in a first configuration2126A at the distal end 2121 of the arm 2122 and at a second position2136B at the base end 2122 of the arm 2120.

Additionally, the tine 2136 can be configured to rotate, which canprovide for picking up an object 120 and depositing the object in thecavity 161 of a bucket 160. For example, the tine 2136 can assume thefirst position 2136A to engage the object 120 and rotate upward tocapture the object 120. The tine 2136 with the captured object 120 cantranslate up the arm 2120 and deposit the object 120 into the cavity 161of the bucket 160.

Although FIG. 32A illustrates a side view of an example embodiment 165Nof an object picking assembly 165 having a single tine 2136 in a firstand second position 2136A, 2136B, further embodiments can comprise anysuitable plurality of tines 2136. For example, some embodiments caninclude a plurality of tines 2136 disposed on respective arms 2120disposed along a length of the front edge 162 of the bucket 160.

FIG. 32B illustrates yet another example embodiment 165O of an objectpicking assembly 165, which includes a tine assembly 2130 that comprisesa first linkage 2131 that extends from a first joint 2132 coupled withinthe cavity 161 of a bucket 160 to a second joint 2133. The tine assembly2130 further comprises a second linkage 2134 that extends from thesecond joint 2133 to a third joint 2137 having one or more tines 2136coupled to the third joint 2137. The tine assembly 2130 furthercomprises a third linkage 2138 that extends from the third joint 2137 toa fourth joint 2139 coupled at a front edge 162 of the bucket 160. Acylinder 2140 can be coupled to the second joint 2133 and a rear end 163of the bucket 160, with the cylinder configured to drive the one or moretines 2136 via the linkages 2131, 2134, 2138. For example, the extensionand retraction of the cylinder 2140 can generate a rotary motion of thetine 2136, which can be used to pick up objects 120 and deposit theobjects 120 into the cavity 161 of the bucket 160.

In various embodiments, it can be desirable to push objects 120 toposition the objects for pick up by an object picking assembly 165; fordistribution within a container such as a cavity 161 of a bucket 160;and the like. For example, FIG. 33 illustrates an example of a pusherassembly 2200 that includes a bar 2210 having a first and second ends2211, 2212. The pusher assembly 2200 can further comprise a firstlinkage 2231 coupled between a first joint 2232 disposed at the firstend 2211 of the bar 2210 and a second joint 2233. The pusher assembly2200 can also include a second linkage 2234 coupled between the secondjoint 2233 and a third joint 2235. The pusher assembly 2200 can alsoinclude a third linkage 2236 coupled between the third joint 2235 and afourth joint 2237 disposed as the second end 2212 of the bar 2210.

An actuator 2220 can be coupled at the first end 2211 of the bar 2210and configured to rotate the first joint 2232 to drive a pusher 2235associated with the third joint 2235. For example, the pusher 2235 canbe driven to push objects 120 for pick up by an object picking assembly165; for distribution within a container such as a cavity 161 of abucket 160; and the like.

As discussed herein, it should be clear that the examples of objectpicking assembly 165 discussed herein should not be limiting on the widevariety of alternative or additional embodiments that are within thescope of the present disclosure. Accordingly, further embodiments caninclude any suitable combination of the example object pickingassemblies 165 discussed herein, including combination, substitution,duplication, or removal of one or more elements, systems or portions ofan object picking assembly 165.

Similarly, object-collection system 618 can be configured in varioussuitable ways including combination, substitution, duplication, orremoval of one or more elements, systems or portions of anobject-collection system 618. For example, FIGS. 34A and 34B illustratefurther example embodiments of object-collection system 618. FIG. 34Aillustrates an object-collection system 618 having a first objectpicking assembly 1651 disposed at a front end of a vehicle 155 with asecond and third object picking assembly 1652, 1653 disposed on sides ofthe of the vehicle 155. The object picking assemblies 165 can beconfigured to pick up objects 120 and deposit the objects 120 into acavity 2361 of a container 2360 in the vehicle 155. In some examples,the object picking assemblies 165 can comprise robotic arms having anysuitable degrees of freedom and various suitable configurations.

FIG. 34B illustrates a further example of an object-collection system618 having a first and second object picking assembly 1654, 1655disposed on sides of the of a vehicle 155. The object picking assemblies165 can be configured to pick up objects 120 and deposit the objects 120onto a conveyor belt 2380, which can convey the objects 120 into acavity 2361 of a container 2360 in the vehicle 155. In some examples,the object picking assemblies 165 can comprise robotic arms having anysuitable degrees of freedom and various suitable configurations.

Referring now to FIGS. 35-42 generally and FIG. 34 specifically, in someimplementations, the object-collection system 2500 includes a vehicle2510 connected to one or more buckets 2600, one or more cameras 2700operatively connected to the vehicle 2510, one or more object pickingassemblies 2800, one or more sensor arrays 2900, one or more processors3000, and one or more memories 3100. An object picking assembly 2800 isconfigured to pick up objects 2810 off of the ground. In someimplementations, the object picking assembly 2800 is disposed at afront-end of the bucket 2600. In other implementations, the objectpicking assembly 2800 is disposed at another section of the bucket 2600,such as the top, side, or rear of the bucket 2600. The object pickingassembly 2800 may be connected directly to the front end of the bucketor may be connected to the front end of the bucket 2600 via a linkageassembly. Correspondingly, the object picking assembly 2800 may beconnected directly to the top, side, or rear of the bucket or may beconnected to the top, side, or rear of the bucket 2600 via a linkageassembly. In still other implementations, the object picking assembly2800 is operatively associated with the bucket 2600, but is actuallyconnected, either directly or indirectly, to another part of theobject-collection system 2500, such as the vehicle 2510.

Referring now to another aspect of the object-collection system 2500,the system further includes one or more sensor arrays 2900. The one ormore sensor arrays, which will be described in further detail below, areused to assist with various functions of the object-collection system2500, including by way of example only, and not by way of limitation,monitoring the terrain being traversed by the object-collection system2500, monitoring the approaching objects, monitoring the functionalityof the object-collection system 2500, and providing feedback on thesuccess and efficiency of the object-collection system 2500 in carryingout its assigned tasks.

In still another aspect of one implementation, the object-collectionsystem 2500 includes a control system with at least one or moreprocessors 3000 and one or more memories 3100. The one or more memories3100 store computer instructions that are executed by the one or moreprocessors 3000 and cause the processors 3000 to carry out variousfunctions. In some implementations, these functions include, by way ofexample only, and not by way of limitation, obtain object informationfor each of one or more identified objects; guide the object-collectionsystem over a target geographical area toward the one or more identifiedobjects based on the object information; capture, via the camera, aplurality of images of the ground relative to the object-collectionsystem as the object-collection system is guided towards the one or moreidentified objects; identify a target object in the plurality of imagesbased on a dataset of trained object parameters; track movement of thetarget object across the plurality of images as the object-collectionsystem is guided towards the one or more identified objects; and employthe tracked movement of the target object to instruct theobject-collection system to pick up the target object.

It will be understood that in other implementations, only some of theabove functions will be carried out by the one or more processors 3000and one or more memories 3100 of the control system. It will also beunderstood that in still other implementations, more than the abovefunctions will be carried out by the one or more processors 3000 and oneor more memories 3100 of the control system. It will further beunderstood that in yet other implementations, alternative and additionalfunction will be carried out by the one or more processors 3000 and oneor more memories 3100 of the control system. Moreover, it will beunderstood that in some implementations, the one or more processors 3000and one or more memories 3100 of the control system are not actuallypart of the object-collection system 2500, but rather are locatedoutside of the system and are operatively associated with theobject-collection system 2500, enabling the transfer of informationbetween the object-collection system 2500 and the control system at itsseparate location.

In other aspect of some implementations, the object picking assembly2800 of the object-collection system 2500 includes an end-effector withtwo or more paddle components 2820, as shown in FIGS. 35, 36, and 38.Each of the two or more paddle components 2820 of the object-collectionsystem 2500 has one or more moving belts 2846 (see FIGS. 41-42). Inanother aspect of some implementations, the one or more moving belts2846 on each of the two or more paddle components 2820 of the objectpicking assembly 2800 move along a path the pulls objects in between thetwo or more paddle components 2820. As shown in FIG. 35, in anotheraspect of some implementations, the two or more paddle components 2820of the object picking assembly 2820 include multiple joints 2830 whichenable repositioning of an object after the object has been picked up.Referring now to FIGS. 37 and 39, in still another aspect of someimplementations, the two or more paddle components 2820 of the objectpicking assembly 2800 include three paddle components 2840. In such animplementation, at least one of the three paddle components 2840 (butpotentially two or more of the paddle components) includes a hinge 2850that enables an object to be pinched so that it may be more easilypicked up and manipulated.

In yet another aspect of some implementations in which the two or morepaddle components 2820 of the object picking assembly 2800 include threepaddle components 2840, the first two of the paddle components are fixedin position with respect to each other, while the third paddle componentis spaced apart from the first two of the paddle components. In somesuch implementations, only the third paddle component 2840 includes ahinge 2850 that enables objects to be pinched so that they may be moreeasily picked up and manipulated.

Referring now FIGS. 35-40, in another aspect of some implementations,the object-collection system 2500 includes one or more sensor arrays2900 that determine whether or not the object picking assembly 2800 wassuccessful in picking up an object. In some implementations, the sensorarray 2900 includes one or more altitude sensors that determine theheight distance between the ground and at least one of the objectpicking assemblies with its associated bucket. This height distancedetermination is significant in that the height distance may becontinuously changing as the vehicle 2510 travels over uneven ground. Inthis regard, the height distance must be known so that the time neededfor the object picking assembly 2800 to contact and pick up the object(e.g., rock, vegetable, fruit, mechanical object, natural object, andthe like) may be determined. The time may be referred to as the stingtime or strike time.

In other aspects of some implementations shown in FIGS. 35-40, theobject-collection system 2500 analyzes a plurality of images taken bythe one or more cameras 2700, identifies object in the plurality ofimages, and tags false negatives. In this regard, a false negative maybe defined as an object 2810 that was not included in one or moreidentified objects in object information that was obtained from anotherpart of a related system. In some implementations, tagging a falsenegative includes dropping virtual pins at locations of the falsenegatives in stored mapping data.

In still other aspects of some implementations of the object-collectionsystem 2500, the movement of a target object is tracked across theplurality of images in stored mapping data and in object informationthat was obtained from another part of a related system. Notably, insome implementations, the object-collection system 2500 applies aparallax correction to assist with picking up a target object at acorrect location. Parallax is a difference in the apparent position ofan object viewed along two different lines of sight (e.g., one line ofsight from the images in stored mapping data and another line of sightfrom the one or more cameras 2700). The parallax is corrected using afunction of an angle of inclination between those two lines of sight. Ifthe object 2810 is unable to be picked up by the object picking assembly2500 due to its size, weight, location, or other parameter, theobject-collection system 2500 leaves the object 2810 at its originallocation and tags the unpicked object by dropping a virtual pin at alocation of the unpicked object in stored mapping data.

Referring now to another aspect of the system shown in FIGS. 35-40, theone or more buckets 2600 of the object-collection system 2500 have awidth, length, and height dimension. In some implementations, as shownin FIG. 40, one or more object picking assemblies 2800 are movablyconnected along a lateral axis to the bucket 2600, enabling the one ormore object picking assemblies 2800 to slide laterally along the widthof the bucket 2600 to assist in positioning the one or more objectpicking assemblies 2800 for picking up objects 2810. Additionally, thebucket 2600 is positioned a height distance above the ground. In someimplementations, as shown in FIGS. 35-38, one or more object pickingassemblies 2800 are movably connected to the bucket 2600 for extensionand retraction, enabling the one or more object picking assemblies 2800to move towards the ground with respect to the bucket 2600 in picking upobjects 2810. As described above, the time it takes for the objectpicking assembly 2800 to move from an initial retracted position to anextended position that contacts an object 2810 to be picked is referredto as the sting time.

In some implementations of the object-collection system 2500, one ormore object picking assemblies 2800 are operatively associated with thebucket 2600 by using one or more picker arms 2550 (e.g., one picker armin FIGS. 35-37 and 39-40, and two picker arms in FIG. 38) to manipulatethe one or more object picking assemblies 2800 with respect to objects2810 to be picked. In another aspect of some implementations, the one ormore picker arms 2550 have one or more degrees of freedom. In anotheraspect of some implementations, the one or more picker arms 2550 areextendable, enabling the one or more object picking assemblies 2800 tomove away from the bucket 2600 and towards an object 2810 to be pickedon the ground. Correspondingly, the one or more picker arms 2550 areretractable, enabling the one or more object picking assemblies 2800 tomove towards the bucket 2600 and away from the ground after an object2810 has been picked. Furthermore, in some implementations, the one ormore picker arms 2550 are extendable and retractable by one segment ofone or more picker arms telescoping within another segment of the one ormore picker arms.

In another aspect of some implementations, the bucket 2600 of theobject-collection system 2500 is rotatably connected to the vehicle2510, enabling the bucket 2600 to rotate and dump objects 2810 that havebeen placed in the bucket 2600. In still another aspect of someimplementations, the bucket 2600 and the one or more object pickingassemblies 2800 are positioned on a front side of the vehicle 2510. Inother implementations, the bucket 2600 and the one or more objectpicking assemblies 2800 are pulled behind the vehicle 2510. In someimplementations, the object-collection system 2500 includes a pluralityof buckets 2600 and a plurality of object picking assemblies 2800.

Additionally, some implementations the object-collection system 2500shown in FIGS. 35 and 40 further include an in-cab display screen 3200that presents a visual representation of the objects 2810 approachingthe vehicle 2510. In another aspect of the object-collection system 2500the control system is connected to the in-cab display screen 3200 andgenerates the visual representation of the objects 2810 approaching thevehicle 2510 from one or more of: the one or more identified objects inobject information, the stored mapping data, and data collected from theone or more cameras 2700, and the data collected from the one or moresensor arrays 2900. In another aspect of some implementations, thevehicle 2510 is driven autonomously along a determined path to pick upidentified objects 2810 using information from one or more of: the oneor more identified objects in object information, the stored mappingdata, and data collected from the one or more cameras 2700, and the datacollected from the one or more sensor arrays 2900.

In still another aspect of some implementations, object picking successis confirmed using load sensors associated with the bucket 2600. In yetanother aspect of some implementations, object picking success isconfirmed using a three dimensional camera system and volumetricestimates. Moreover, in yet another aspect of some implementations, theobject-collection system 2500 includes a rear facing camera to identifyobjects that failed to be picked up by the object-collection system.

In another implementation, the object-collection system 2500 includesone or more buckets 2600 connected to a vehicle 2510, one or more objectpicking assemblies 2800, one or more processors 3000, and one or morememories 3100. An object picking assembly 2800 is configured to pick upobjects 2810 off of the ground. In some implementations, the objectpicking assembly 2800 is disposed at a front-end of the bucket 2600. Inother implementations, the object picking assembly 2800 is disposed atanother section of the bucket 2600, such as the top, side, or rear ofthe bucket 2600. The object picking assembly 2800 may be connecteddirectly to the front end of the bucket or may be connected to the frontend of the bucket 2600 via a linkage assembly. Correspondingly, theobject picking assembly 2800 may be connected directly to the top, side,or rear of the bucket or may be connected to the top, side, or rear ofthe bucket 2600 via a linkage assembly. In still other implementations,the object picking assembly 2800 is operatively associated with thebucket 2600, but is actually connected, either directly or indirectly,to another part of the object-collection system 2500, such as thevehicle 2510.

In another aspect of some implementations, the object-collection system2500 includes a control system with at least one or more processors 3000and one or more memories 3100. The one or more memories 3100 storecomputer instructions that are executed by the one or more processors3000 and cause the processors 3000 to carry out various functions. Insome implementations, these functions include, by way of example only,and not by way of limitation, obtain object information for each of oneor more identified objects; guide the object-collection system over atarget geographical area toward the one or more identified objects basedon the object information; receive a plurality of images of the groundrelative to the object-collection system as the object-collection systemis guided towards the one or more identified objects; identify a targetobject in the plurality of images based on a dataset of trained objectparameters; track movement of the target object across the plurality ofimages as the object-collection system is guided towards the one or moreidentified objects; and employ the tracked movement of the target objectto instruct the object-collection system to pick up the target object.

Referring now to FIGS. 41A-45C, several additional implementations ofthe object-collection system 2500 are shown. Specifically, FIGS. 41A,41B, and 41C shown an object picking assembly 2800 that includes threepaddle components 2840 and rotating belts 2846 on each paddle component.As shown in FIGS. 44, 45A, 45B, and 45C, in some implementations thereis more than one rotating belt associated with each paddle component. Byemploying three paddle components 2840, and one or more rotating belts2846 on each paddle component, the object picking assembly 2800 is ableto pinch, re-orient, and manipulate objects during a collection process.In some implementations in which one or more paddle components havemultiple rotating belts 2846, the belts are capable of rotating atdifferent speeds, in different directions, or both, which assists inre-orienting and manipulating objects during a collection process.Additionally, as shown in FIGS. 41A, 41B, 41C, and 42 a hinge 2850 maybe used to associate the third paddle component with at least one of theother two paddle components. In other implementations, other types oflinkages with multiple components and joints may be used in more complexarrangements to provide a greater number of degrees of freedom to thethird paddle component. Such multi-components linkages may includenumerous arms and joints, telescoping components, multiple belts, andcombinations thereof to provide advanced positioned and manipulationcapabilities.

Operations of processes described herein can be performed in anysuitable order unless otherwise indicated herein or otherwise clearlycontradicted by context. The processes described herein (or variationsand/or combinations thereof) are performed under the control of one ormore computer systems configured with executable instructions and areimplemented as code (e.g., executable instructions, one or more computerprograms or one or more applications) executing collectively on one ormore processors, by hardware or combinations thereof. In an embodiment,the code is stored on a computer-readable storage medium, for example,in the form of a computer program comprising a plurality of instructionsexecutable by one or more processors. In an embodiment, acomputer-readable storage medium is a non-transitory computer-readablestorage medium that excludes transitory signals (e.g., a propagatingtransient electric or electromagnetic transmission) but includesnon-transitory data storage circuitry (e.g., buffers, cache, and queues)within transceivers of transitory signals. In an embodiment, code (e.g.,executable code or source code) is stored on a set of one or morenon-transitory computer-readable storage media having stored thereonexecutable instructions that, when executed (i.e., as a result of beingexecuted) by one or more processors of a computer system, cause thecomputer system to perform operations described herein. The set ofnon-transitory computer-readable storage media, in an embodiment,comprises multiple non-transitory computer-readable storage media, andone or more of individual non-transitory storage media of the multiplenon-transitory computer-readable storage media lack all of the codewhile the multiple non-transitory computer-readable storage mediacollectively store all of the code. In an embodiment, the executableinstructions are executed such that different instructions are executedby different processors—for example, a non-transitory computer-readablestorage medium store instructions and a main CPU executes some of theinstructions while a graphics processor unit executes otherinstructions. In an embodiment, different components of a computersystem have separate processors, and different processors executedifferent subsets of the instructions.

Accordingly, in an embodiment, computer systems are configured toimplement one or more services that singly or collectively performoperations of processes described herein, and such computer systems areconfigured with applicable hardware and/or software that enable theperformance of the operations.

The various embodiments described above can be combined to providefurther embodiments. These and other changes can be made to theembodiments in light of the above-detailed description. In general, inthe following claims, the terms used should not be construed to limitthe claims to the specific embodiments disclosed in the specificationand the claims, but should be construed to include all possibleembodiments along with the full scope of equivalents to which suchclaims are entitled. Accordingly, the claims are not limited by thedisclosure. All references, including publications, patent applications,and patents, cited herein are hereby incorporated by reference to thesame extent as if each reference were individually and specificallyindicated to be incorporated by reference and were set forth in itsentirety herein.

1. A method, comprising: receiving a plurality of target imagesassociated with a target set of conditions in which to detect objects;generating object identification information based on an analysis of theplurality of target images using a first neural network trained on afirst set of conditions and a second neural network trained on a secondset of conditions; and selecting the first neural network or the secondneural network as a preferred neural network for the target set ofconditions based on an analysis of the object identificationinformation.
 2. The method of claim 1, wherein generating the objectidentification information includes: analyzing the plurality of targetimages using the first neural network to detect objects in the pluralityof target images resulting in a first set of object identificationinformation; analyzing the plurality of target images using the secondneural network to detect objects in the plurality of target imagesresulting in a second set of object identification information; anddetecting differences between the first and second sets of objectidentification information based on a comparison between the first setof object identification information and the second set of objectidentification information.
 3. The method of claim 1, wherein generatingthe object identification information includes: analyzing a first set oftarget images from the plurality of target images using the first neuralnetwork to detect objects in the first set target images resulting in afirst set of object identification information; analyzing a second setof target images from the plurality of target images using the secondneural network to detect objects in the second set of target imagesresulting in a second set of object identification information; andcomparing the first set of object identification information with thesecond set of object identification information to detect differencesbetween the first and second sets of object identification information.4. The method of claim 3, further comprising: alternatingly selectingthe first and second sets of target images from the plurality of targetimages based on a select number of images for each selection.
 5. Themethod of claim 1, wherein selecting the first neural network or thesecond neural network as the preferred neural network includes:selecting the preferred neural network as the first neural network inresponse to a first accuracy of the first neural network detectingobjects in the plurality of target images being higher than a secondaccuracy of the second neural network detecting objects in the pluralityof target images.
 6. The method of claim 1, wherein selecting the firstneural network or the second neural network as the preferred neuralnetwork includes: selecting the first neural network as the preferredneural network in response to a first number of positive objectidentifications using the first neural network being higher than asecond number of positive object identifications using the second neuralnetwork; and selecting the second neural network as the preferred neuralnetwork in response to the second number of positive objectidentifications using the second neural network being higher than thefirst number of positive object identifications using the first neuralnetwork.
 7. The method of claim 1, wherein selecting the first neuralnetwork or the second neural network as the preferred neural networkincludes: selecting the first neural network as the preferred neuralnetwork in response to a first number of false-positive objectidentifications using the first neural network being lower than a secondnumber of false-positive object identifications using the second neuralnetwork; and selecting the second neural network as the preferred neuralnetwork in response to the second number of false-positive objectidentifications using the second neural network being lower than thefirst number of false-positive object identifications using the firstneural network.
 8. The method of claim 1, wherein selecting the firstneural network or the second neural network as the preferred neuralnetwork includes: providing the first and second sets of objectidentification information to a reviewer; and receiving a selection ofthe preferred neural network from the reviewer based on the first andsecond sets of object identification information.
 9. The method of claim1, further comprising: receiving a second plurality of target imagesassociated with a third set of conditions; and predicting use of thefirst neural network or the second neural network to analyze the secondplurality of target images based on a comparison between the third setof conditions and first, second, and target sets of conditions.
 10. Themethod of claim 9, wherein predicting use of the first neural network orthe second neural network further comprises: determining separatecloseness factors between the third set of conditions and each of thefirst, second, and target sets of conditions; selecting a highestcloseness factor from the determined closeness factors; in response tothe highest closeness factor being associated with the first set ofconditions, analyzing the second plurality of target images using thefirst neural network to detect objects in the second plurality of targetimages; in response to the highest closeness factor being associatedwith the second set of conditions, analyzing the second plurality oftarget images using the second neural network to detect objects in thesecond plurality of target images; and in response to the highestcloseness factor being associated with the target set of conditions,analyzing the second plurality of target images using the preferredneural network to detect objects in the second plurality of targetimages.
 11. The method of claim 1, further comprising: receiving aselection of a third set of conditions in which to detect objects;determining separate closeness factors between the third set ofconditions and each of the first, second, and target sets of conditions;selecting a highest closeness factor from the determined closenessfactors; in response to the highest closeness factor being associatedwith the first set of conditions, selecting the first neural network asa second preferred neural network to detect objects in images associatedwith the third set of conditions; in response to the highest closenessfactor being associated with the second set of conditions, selecting thesecond neural network as the second preferred neural network to detectobjects in images associated with the third set of conditions; and inresponse to the highest closeness factor being associated with thetarget set of conditions, selecting the preferred neural network as thesecond preferred neural network to detect objects in images associatedwith the third set of conditions.
 12. The method of claim 11, furthercomprising: receiving a second plurality of target images associatedwith the third set of conditions; and analyzing a second plurality oftarget images using the second preferred neural network to detectobjects in the second plurality of target images.
 13. The method ofclaim 1, wherein the first and second sets of conditions include atleast two of: a geographical area, expected dirt type found in thegeographical area, expected object type found in the geographical area,type of crop being planted at the geographical area, status of the crop,time of year, weather at time of image capture, and expected type oramount of non-cultivated vegetation.
 14. The method of claim 1, whereinthe first and second sets of conditions include at least one of:geological information, topographical information, biologicalinformation, or climatic information.
 15. The method of claim 1, furthercomprising: training the first neural network by: receiving a firstselection of the first set of conditions from a user; obtaining a firstset of training images associated with the first set of conditions; andtraining the first neural network using the first set of trainingimages; and training the second neural network by: receiving a secondselection of the second set of conditions from the user; obtaining asecond set of training images associated with the second set ofconditions; and training the second neural network using the second setof training images.
 16. The method of claim 1, further comprising:accessing the trained first neural network and the trained second neuralnetwork to perform the analysis of the plurality of target images.
 17. Acomputing device, comprising: a neural network database that stores afirst neural network trained on a first set of conditions and secondneural network trained on a second set of conditions; a processor; and amemory that stores computer instructions that, when executed by theprocessor, cause the processor to: receive a plurality of target imagesassociated with a target set of conditions in which to detect objects;generate a first set of identification information based on an analysisof the plurality of target images using the first neural network todetect objects in the plurality of target images; generate a second setof identification information based on an analysis of the plurality oftarget images using the second neural network to detect objects in theplurality of target images; and select the first neural network or thesecond neural network as a preferred neural network for the target setof conditions based on a comparison between the first and second sets ofidentification information.
 18. The computing device of claim 17,wherein execution of the computer instructions by the processor toanalyze the plurality of target images using the first and second neuralnetwork causes the processor to: alternatingly select a first set oftarget images and a second set of target images from the plurality oftarget images; analyze the first set of target images using the firstneural network to identify objects in the first set of target images;and analyze the second set of target images using the second neuralnetwork to identify objects in the second set of target images.
 19. Thecomputing device of claim 17, wherein execution of the computerinstructions by the processor to select the first neural network or thesecond neural network as the preferred neural network causes theprocessor to: select the preferred neural network based on firstaccuracy of the first neural network and a second accuracy of the secondneural network.
 20. The computing device of claim 17, wherein executionof the computer instructions by the processor causes the processor to:receive a third set of conditions in which to detect objects; andpredict use of the first neural network or the second neural networkbased on a comparison between the third set of conditions and first,second, and target sets of conditions.
 21. A non-transitorycomputer-readable storage medium that stores computer instructions that,when executed by a processor on a computer device, cause the processorto: receive a plurality of target images associated with a target set ofobject picking conditions; analyze the plurality of target images usinga plurality of neural networks that are each trained on different setsof conditions to detect objects in the plurality of target images; andselect, from the plurality of neural networks, a preferred neuralnetwork for the target set of conditions based on the analysis of theplurality of target images using the plurality of neural networks.